{
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "#**Hyperparameters Optimized Deep Learning Models for Electrocardiogram Signals Classification: Performance Evaluation Based on the MIT-BIH Dataset**"
      ],
      "metadata": {
        "id": "CXL8WQzhxZx0"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "I8LWA7G84y36",
        "outputId": "2b7e5c4e-857f-43ec-908f-54bad9bdeb3c"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting wfdb\n",
            "  Downloading wfdb-4.0.0-py3-none-any.whl (161 kB)\n",
            "\u001b[K     |████████████████████████████████| 161 kB 5.2 MB/s \n",
            "\u001b[?25hRequirement already satisfied: requests<3.0.0,>=2.8.1 in /usr/local/lib/python3.7/dist-packages (from wfdb) (2.23.0)\n",
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            "Requirement already satisfied: python-dateutil>=2.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib<4.0.0,>=3.2.2->wfdb) (2.8.2)\n",
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            "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.7/dist-packages (from matplotlib<4.0.0,>=3.2.2->wfdb) (0.11.0)\n",
            "Requirement already satisfied: typing-extensions in /usr/local/lib/python3.7/dist-packages (from kiwisolver>=1.0.1->matplotlib<4.0.0,>=3.2.2->wfdb) (4.1.1)\n",
            "Requirement already satisfied: pytz>=2017.3 in /usr/local/lib/python3.7/dist-packages (from pandas<2.0.0,>=1.0.0->wfdb) (2022.1)\n",
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            "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.7/dist-packages (from requests<3.0.0,>=2.8.1->wfdb) (2.10)\n",
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            "Installing collected packages: wfdb\n",
            "Successfully installed wfdb-4.0.0\n",
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting opendatasets\n",
            "  Downloading opendatasets-0.1.22-py3-none-any.whl (15 kB)\n",
            "Requirement already satisfied: kaggle in /usr/local/lib/python3.7/dist-packages (from opendatasets) (1.5.12)\n",
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            "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.7/dist-packages (from requests->kaggle->opendatasets) (2.10)\n",
            "Installing collected packages: opendatasets\n",
            "Successfully installed opendatasets-0.1.22\n",
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Requirement already satisfied: kaggle in /usr/local/lib/python3.7/dist-packages (1.5.12)\n",
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            "Requirement already satisfied: tqdm in /usr/local/lib/python3.7/dist-packages (from kaggle) (4.64.0)\n",
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            "Requirement already satisfied: text-unidecode>=1.3 in /usr/local/lib/python3.7/dist-packages (from python-slugify->kaggle) (1.3)\n",
            "Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.7/dist-packages (from requests->kaggle) (3.0.4)\n",
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            "\u001b[K     |████████████████████████████████| 135 kB 5.1 MB/s \n",
            "\u001b[?25h"
          ]
        }
      ],
      "source": [
        "! pip install wfdb\n",
        "\n",
        "%load_ext autoreload\n",
        "%autoreload 2\n",
        "\n",
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "import os\n",
        "import datetime\n",
        "import itertools\n",
        "import wfdb\n",
        "from datetime import datetime\n",
        "\n",
        "import IPython\n",
        "import IPython.display\n",
        "from IPython.display import display\n",
        "import matplotlib.pyplot as plt\n",
        "%matplotlib inline\n",
        "\n",
        "import shutil\n",
        "import posixpath\n",
        "\n",
        "from wfdb import processing\n",
        "\n",
        "import time\n",
        "import random\n",
        "\n",
        "import pandas as pd\n",
        "import matplotlib.colors as mcolors\n",
        "import seaborn as sns\n",
        "\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.metrics import precision_recall_curve\n",
        "from sklearn.metrics import classification_report, confusion_matrix\n",
        "from sklearn.metrics import accuracy_score, auc, f1_score, precision_score, recall_score, roc_curve\n",
        "from sklearn.svm import SVC\n",
        "from sklearn.ensemble import RandomForestClassifier, ExtraTreesClassifier\n",
        "from sklearn.metrics import accuracy_score, classification_report, confusion_matrix, plot_confusion_matrix\n",
        "from sklearn import tree\n",
        "from sklearn.multiclass import OneVsOneClassifier, OneVsRestClassifier\n",
        "\n",
        "from keras.models import load_model\n",
        "from keras.utils.vis_utils import plot_model\n",
        "from keras.models import Model\n",
        "\n",
        "from scipy.signal import resample\n",
        "\n",
        "import warnings\n",
        "warnings.filterwarnings('ignore')\n",
        "\n",
        "!pip install opendatasets\n",
        "import opendatasets as od\n",
        "\n",
        "!pip install kaggle\n",
        "\n",
        "from sklearn.utils import resample\n",
        "\n",
        "import imblearn\n",
        "from imblearn.over_sampling import SMOTE\n",
        "\n",
        "import tqdm, re, sys\n",
        "\n",
        "from keras.layers import Conv2D, Conv1D, MaxPooling2D, MaxPooling1D, Flatten, BatchNormalization, Dense, LSTM, Dropout, Bidirectional\n",
        "from keras.utils.np_utils import to_categorical\n",
        "from keras.models import Sequential\n",
        "from keras.callbacks import CSVLogger, ModelCheckpoint\n",
        "from keras.layers import Layer, SpatialDropout1D\n",
        "\n",
        "!pip install -q -U keras-tuner\n",
        "import kerastuner as kt\n",
        "import keras_tuner\n",
        "from kerastuner.tuners import RandomSearch\n",
        "\n",
        "from keras import backend as K\n",
        "\n",
        "import tensorflow as tf\n",
        "from tensorflow import keras\n",
        "from tensorflow.keras.optimizers import Adam"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "A0gtwVBmEtiR",
        "outputId": "efde57e7-7ad9-47c0-d27b-f5bc98650779"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'/content'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 2
        }
      ],
      "source": [
        "os.getcwd()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fpcZP6FhQDJ0",
        "outputId": "249a838b-bd1f-4130-e5f2-88e25010f3d7"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading mitbih-with-synthetic.zip to /content\n",
            " 94% 139M/148M [00:01<00:00, 133MB/s]\n",
            "100% 148M/148M [00:01<00:00, 120MB/s]\n",
            "Downloading heartbeat.zip to /content\n",
            " 92% 91.0M/98.8M [00:00<00:00, 99.6MB/s]\n",
            "100% 98.8M/98.8M [00:00<00:00, 110MB/s] \n"
          ]
        }
      ],
      "source": [
        "\"\"\"\n",
        "   Obtain the MITBIH Dataset from kaggle website:\n",
        "\"\"\"\n",
        "os.environ['KAGGLE_USERNAME'] = \"edgariyasele\" # username from the json file\n",
        "os.environ['KAGGLE_KEY'] = \"\" # key from the json file\n",
        "#!kaggle datasets download -d kaggle kernels pull polomarco/ecg-classification-cnn-lstm-attention-mechanism\n",
        "!kaggle datasets download -d polomarco/mitbih-with-synthetic\n",
        "!kaggle datasets download -d shayanfazeli/heartbeat\n",
        "\n",
        "## Unzip the downloaded data:\n",
        "from zipfile import ZipFile\n",
        "zf = ZipFile('/content/heartbeat.zip', 'r')\n",
        "zf.extractall('/content')\n",
        "zf.close()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "x22Z_3FAo4LV",
        "outputId": "98f4ddda-3af8-4ea7-c082-64c9e1e628b3"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Time taken: 8.672 seconds\n",
            "Data loaded........\n"
          ]
        }
      ],
      "source": [
        "\"\"\"\n",
        "   Load the Data:\n",
        "\"\"\"\n",
        "# loading all data files into memory\n",
        "start = time.time()\n",
        "\n",
        "data_mitbih_train = pd.read_csv('/content/mitbih_train.csv', header=None)\n",
        "data_mitbih_test = pd.read_csv('/content/mitbih_test.csv', header=None)\n",
        "\n",
        "end = time.time()\n",
        "print('Time taken: %.3f seconds' % (end-start))\n",
        "\n",
        "print('Data loaded........')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "dqmLXXWbt4ZX",
        "outputId": "6c359f43-6893-4039-b165-c54a61e2a176"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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            "dtypes: float64(188)\n",
            "memory usage: 125.6 MB\n",
            "None\n",
            "(87554, 188)\n",
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            "max        1.000000      1.000000      1.000000      1.000000      1.000000   \n",
            "\n",
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            "mean       0.210399      0.205808      0.201773      0.198691      0.196757   \n",
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            "max        1.000000      1.000000      1.000000      1.000000      1.000000   \n",
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            "\n",
            "                187  \n",
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        },
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          "output_type": "execute_result",
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            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ],
      "source": [
        "\"\"\"\n",
        "   Visualize the MITBIH Train Data:\n",
        "\"\"\"\n",
        "print(data_mitbih_train.info())\n",
        "print(data_mitbih_train.shape)\n",
        "print(data_mitbih_train.describe())\n",
        "data_mitbih_train"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "UpAMaHVmucEX",
        "outputId": "7240c75e-2a26-4303-f922-e8550a7df475"
      },
      "outputs": [
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          "output_type": "stream",
          "name": "stdout",
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            "None\n",
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          },
          "metadata": {},
          "execution_count": 7
        }
      ],
      "source": [
        "\"\"\"\n",
        "   Visualize the MITBIH Test Data:\n",
        "\"\"\"\n",
        "print(data_mitbih_test.info())\n",
        "print(data_mitbih_test.shape)\n",
        "print(data_mitbih_test.describe())\n",
        "data_mitbih_test"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1El_MZYT33eU",
        "outputId": "e3382fa6-7a87-47cd-d2a9-f80369d59599"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "0.0    72471\n",
              "4.0     6431\n",
              "2.0     5788\n",
              "1.0     2223\n",
              "3.0      641\n",
              "Name: 187, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ],
      "source": [
        "\"\"\"\n",
        "   How many classes are there in each dataset:\n",
        "\"\"\"\n",
        "# The MIT dataset has 5 clases; 0 = N (Normal Beat); 1 = S (Supraventricular premature beat); 2 = V (Premature ventricular contraction);\n",
        "# 3 = F (Fusion of ventricular and normal beat); 4 = Q (Unclassifiable beat)\n",
        "\n",
        "# MITBIH Classes:\n",
        "data_mitbih_train[187].value_counts()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "PNGkaFby5o-s",
        "outputId": "00932833-ee9f-446c-a43c-339126728fcd"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1440x720 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1440x864 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "\"\"\"\n",
        "   Exploratory Data Analysis (EDA): - MITBIH\n",
        "\"\"\"\n",
        "## Rename the last column as 'class'\n",
        "data_mitbih_train.rename(columns={187: 'class'}, inplace=True)\n",
        "\n",
        "# Setting Dictionary to define the type of Heartbeat for both datasets\n",
        "MIT_Outcome = {0. : 'Normal Beat',\n",
        "               1. : 'Supraventricular premature beat',\n",
        "               2. : 'Premature ventricular contraction',\n",
        "               3. : 'Fusion of ventricular and normal beat',\n",
        "               4. : 'Unclassifiable beat'}\n",
        "\n",
        "data_mitbih_train['label'] = data_mitbih_train.iloc[:, -1].map(MIT_Outcome)\n",
        "\n",
        "## Plot the data:\n",
        "\n",
        "plt.figure(figsize = (20, 10))\n",
        "my_circle = plt.Circle((0, 0), 0.7, color='white')\n",
        "plt.pie(data_mitbih_train['class'].value_counts(), labels=['Normal Beat', 'Unclassifiable Beats', 'Premature Ventricular Contraction',\n",
        "                                                   'Supraventricular Premature Beat', 'Fusion of Ventricular and Normal Beat'],\n",
        "        colors=['red', 'green', 'blue', 'skyblue', 'orange'], autopct = '%1.1f%%', textprops={'fontsize': 17, })\n",
        "p = plt.gcf()\n",
        "p.gca().add_artist(my_circle)\n",
        "plt.title('MITBIH Showing the Type of Heartbeats in a Pie Chart', size = 26, fontweight='bold')\n",
        "plt.savefig('MITBIH Showing the Type of Heartbeats in a Pie Chart.png')\n",
        "plt.show()\n",
        "\n",
        "## Plot the Data - 2\n",
        "\n",
        "# making the class labels for the dataset\n",
        "data_0 = data_mitbih_train.iloc[0, 0:187]\n",
        "data_1 = data_mitbih_train[data_mitbih_train['class'] == 1]\n",
        "data_2 = data_mitbih_train[data_mitbih_train['class'] == 2]\n",
        "data_3 = data_mitbih_train[data_mitbih_train['class'] == 3]\n",
        "data_4 = data_mitbih_train[data_mitbih_train['class'] == 4]\n",
        "\n",
        "sns.set_style('darkgrid')\n",
        "fig, ax = plt.subplots(figsize=(20, 12))\n",
        "ax.plot(data_0, color='green', label='Normal Heartbeats')\n",
        "ax.plot(data_1.iloc[0, 0:187], color='blue', label='Supraventricular Heartbeats')\n",
        "ax.plot(data_2.iloc[0, 0:187], color='red', label='Premature ventricular contraction')\n",
        "ax.plot(data_3.iloc[0, 0:187], color='skyblue', label='Fusion of ventricular and normal beat')\n",
        "ax.plot(data_4.iloc[0, 0:187], color='orange', label='Unclassifiable beat')\n",
        "ax.xaxis.set_major_locator(plt.MaxNLocator(10))\n",
        "ax.text(0.0, 0.1, \" \", fontsize=22, transform=ax.transAxes)\n",
        "ax.set_xlabel('Time', weight='bold').set_fontsize('18')\n",
        "ax.set_ylabel('Amplitude', weight='bold').set_fontsize('18')\n",
        "ax.set_title('MITBIH Showing the Type of Heartbeats', size = 26, fontweight='bold')\n",
        "ax.legend(loc = 'best', bbox_to_anchor = (1.0, 1.0), ncol = 1, frameon=True, fontsize = '22')\n",
        "plt.savefig('MITBIH Showing the Type of Heartbeats.png')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 601
        },
        "id": "kAeaN5LX7Bx3",
        "outputId": "65ec017a-7a91-479b-f9a8-fed2584149a6"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1440x720 with 1 Axes>"
            ],
            "image/png": 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xY3vjxg3Geu/du4eKigrGOUp8Ga4hV7JqyLGyfPly7N+/n1GWnp6O9PR0RERE4Kuvvmpw/RRp+/bt2LBhA6NXak5ODkJDQ3Hu3Dns37+f82a9qqoKq1evxoEDB1g94fPz85Gfn49r165h165djX6Mjx07lhF4un//Pm7fvs05LOzMmTOM171794apqanEdZeXlyMoKIiVdqImYHH69Gn89ddfnD0OX716he+++47zYj8nJwc5OTmIiYlBcHAwNm/ezJpBSRYNOTbfRw1tM5XZbv388884ePCgfF9MSnFxcZg5cyZKS0tFZa9fv0ZERASio6OxatUqiQ+7jh49ihUrVrAewPN4PBQVFeHevXvYu3cvFi5cyJk4OjU1FXPnzuVcN5/PR1lZGbKzsxEbG4udO3di69atsLOza8C3fevmzZuYMmUKCgsLWfXOzs5GdnY2UlJSEBoaCl9fX6xfv77B25RGTk4ORo4cyQrKpqWlIS0tDfHx8di+fbtc7XdT3Z/VhWsmO/HzjjQao03Jyclhnbs1NTXx/fffS/V5HR2depcJCwvDTz/9xJhkSZprwbquDWquTePj47Fz506sWrUKAwYMkKrOO3bswO+//67QEWvi5L4SHTNmDONkXDuReO3/19HRwdChQ+XdTJMpLy/H+fPnceDAAUa5iYkJPD09m6hWjUdTUxODBg1ilcfHx8u9zri4OCxdurQBtWra7Yrn+2rXrh1sbGxY0wNnZ2cjKSmpwduT16+//ooNGzbUO8a4tLQUM2bMkGpGlNzcXPzwww+MoFNDcAXxvL29OZ+oNkaS8ZKSEkyePJkRdKqturoaK1aswL1792Re982bN1lBpxpCoRCrVq3CTz/9xAo61bZ161bOfDxnz57FV199xbqAFicQCLBt2zasWbNGqjrPnDmTdSNd25EjRxgBwsaQm5uLr7/+Gnl5eY2yvby8PAQGBnIGncSVlJRg9uzZUu2rt27dwsqVKyUen5WVlVi4cKFcOQK4HoosW7YM/fr1w7x583DgwAHcvn2bFaCS1ueff87KAVjbH3/8gUePHrHKo6KiMHPmTKkuMFNSUhAYGMg4FtXU1FjD28XPseKveTweI+/M06dPWbPsKiLwJO+xEhwczAo61Xb//n389NNPDa6fosTGxuL333+XmOOwoKBAlNOjNqFQiDlz5rB6BnPJzc3FxIkTcfv2bYXUWRp9+/ZlBVJCQ0NZy+Xk5CAlJYVRVt+17fr16+vMdZqcnIz58+ezyt+8eYPAwECJT5hrS0lJwfjx41FUVFTvslwaemy+b5TVZkoiS7t15swZpQed3rx5gxkzZjCCTrXxeDzMnz+f8xgMDg7G4sWL6x31wePxsGzZsjrPb9J48uQJpk2b1uDJjYRCIebNm8cKOr0LFi9eXGdPwIsXL7J67ipDY92fied4AgB9fX2Z1tFYbQpXDtpevXopLB/gvXv3sHDhQokzezfkWrBGaWkpvvvuOzx48KDeZVNTU7Fu3TqlBp2ABvR4atOmDTw8PHDlyhUAb3s5/fDDD3j+/DnjQt3Pz++9yR+xefNmidFh4O2whU2bNsndy0OS+rq1NhWu3DqZmZmsMgsLC3Tv3h2tW7eGkZER9PX1UVFRgWfPniEqKorRPTgiIgIzZ84U3SDVJJHbsmUL44aFK0G8+JPUhmxXVikpKazvXhNw8vHxwW+//cZ4LyIignWjVJMAlSvhKVdSQltbWzx8+JA15HH69Olo164dZz0vX76MvXv3MsosLS0xcuRIWFtbIysrCwcPHhQNfRUKhViwYAFiY2PrPPnXnBgtLCwQEBAAa2tr5OTkyB1gEw/iWVhYwMXFBSoqKujUqRPjHHLmzBn8+OOPcnc3lsa2bdtYDaKJiQk+//xztGjRAlevXsWJEyfkfjKjrq6OwMBA2NvbIzIyktGgVVVV4ciRIwDeduHt0qULzp07h9jYWNEyAoEAYWFhjJuUN2/eYP78+YxGQkdHByNGjED79u1RUlKC0NBQRsBq9+7d6N+/f71PfyorK2FtbY3AwEAYGBjg0KFDrGGmx44dQ+/evQG87TXQp08fJCYmsmYzXbx4MaOhFu86r62tDTc3Nzg5OcHY2BhGRkYQCoXIz8/H1atXcfnyZdGyBQUFOHjwoKj7ekO2W59ly5axhja0a9cOAQEB0NfXx5UrV1iBpiVLlsDDw6POXhA1x5Knpyf69++PvLw8bNu2jXEx//z5c1y+fBn9+vWTqc69evWCrq4u66bi1atXCA8PR3h4OABAV1cXffv2xaeffioxPw6XoqIiODo6IiAgAGpqati9ezdjeF91dTVCQ0MZTwTz8/OxaNEi1rr8/PzQq1cvFBcX49ixY4wLoxcvXmDZsmWMbu7u7u74999/Ra9rn3uEQiFngDApKQndu3cHwO7tpKmpKeql3RCyHivA2/14w4YNrHX1798f/fv3F/VUk3YGHUlqJ2pdvnw5I+VBt27dMHbsWMbydSVxLSoqgoqKCvz9/dGjRw/R8IDaQczk5GQ8evSI0UPh6NGjOHv2LGNddnZ2GDZsGExNTfHw4UMcPnxYNCyhsrISc+fORVRUVKPkB1JVVcXo0aMZPRwiIyOxcOFCRptz9uxZxrlWT08PXl5eda67uLgYRkZGmDBhAqytrXH37l0cOnSI8QAnJiYGcXFxjP3jl19+wdOnTxnr6t27N/r164dmzZohMTERERERovo8ffoUq1atwq+//irTd1fEsSnLdU1DLVy4sEE9jxXZZjak3ZKk5rzt4uKCTz75BPr6+sjIyACfz8eoUaNQXl6OQ4cO4dq1a6LP1E7iXINryHiNmuOsX79+8PHxQWVlJY4fP467d++KluHz+VixYgWjTX306BErCGdkZISAgADY2NggNzcXR44cYZyzVq1ahX79+rGuu/X19dG1a1c4OjqKfjsej4fc3FycP3+e9cAgIiJCNNRKlv2tpv1//Pgxnjx5wnhv4MCB6NmzJ7S1tVFcXIzMzEwkJSXJ9XCxISorK2FsbIwJEybA0tIS4eHhrAf8x44dw6effirX+hvzPqk+RUVFnKkROnToINN6GqtNEd9nAMVOkFBzPyHPtaCGhgY6duwIV1dXmJqawsjICOrq6igoKMCNGzcY7VVVVRV27dpV7/DbmvOPra0thg8fDjMzM2RnZ+PZs2cYPny4zMedJA1KLj527FhR4KmiogJhYWF4/PgxYxmumUbeV4GBgR9UomOunUc8V9ekSZPqbEwnTJiAvn37il4LBAJcuXJFdBKtSSJ3+PBhRuCpvgTxDd2urLh6MtQMUWvbti3s7e0ZF2enT5/GokWLGPkbam7wuBKecn1XBwcHJCQksAJPPXr0EN1MiduxYwfjdcuWLXH8+HEYGRmJyvz9/eHv7y+6AS4sLERISAi+/PJLznXWsLW1xaFDhxjrkkdaWhor+u7t7S3q6eTj48O4iSwsLMTFixdZPcsURSAQsHobqaurY//+/aIbqBEjRsDMzEzuIcOzZs3ClClTALz9W3fv3p31FO/TTz8VDckZMWIE+vXrx+hdcufOHcbyBw8eZAQY1NXV8ffffzNuHseNG4eRI0cyfu+dO3fWG3gyMzPDsWPHROeAwYMHw8fHB/n5+aJlaj/979ixIzp27IjKykpWAMjT01PihbCFhQWuXbsmmvVI3OTJkzFx4kTGRfzly5dFx768263Ps2fPWMdoq1atEBISIuqKPnLkSFhYWOCvv/4SLVNWVibVDcaIESMYs4S2bNmSlWvp9u3bMgeeDAwMsGDBAs6eJ7WVlpbi1KlTOHXqFHx8fLB69Wqputjb29sjJCRE9PDF09MT3t7ejCdy4r1CDh48yMp1MGnSJEZwKiAgAEOHDmUEsc6cOYNnz56JLoLFeye9ePECL168gKWlJR48eCDq8aGhoSG6sa8dbBIPPHXq1AlaWlr1fuf6yHqsAMCJEydYvQb8/f0Z+YOGDBmCQYMGSeyZII3aiVrXrVvHaL9btmwpcxLXb775Bt98843otZ6eHuuiMyUlhRF4Em+TXF1dsX//fsZv379/f8awnMzMTJw9e5az17UyjBo1Cps3bxbtN4WFhYiJiWEMnRU/HwwcOLDeh5BqamrYt28fHBwcALxNO9GhQwdW8OTIkSOiwFN2djaioqIY73/22WeMANHo0aNhY2PDCMyGh4fj22+/lZjPg4sijk1Zr2uakqLazIa2W3Xx8/PDb7/9JnFo1aVLlxiBJ3mSMQ8ePJgR+A4ICMCwYcMY92/JycmMiXLEg8wGBgY4duwYI0gREBCAIUOGiHrE8Xg87NmzBz/++KNomc6dOyMhIUFiAGDatGkYPHgwMjIyRGWXL18WBZ7k2d/Eezo5ODhw5u4B3vaQqb1tZdPU1MThw4fRtm1bAG/P+yNGjGAEP+/duwcejydxf5Okse+TaisrKxM9YBYIBHj27BlCQkJYvY719fVlvs5prDaFKze1omc/lfda8Pr16xLbn6CgICxZsgQhISGistrnorp069YNu3btknhtpIjzfIMCT15eXmjRooXo5ujgwYOMncrZ2RnOzs7IyspqyGbeGVu2bMGlS5cQHBzc6AkwmwJXdzvxYVDa2tqiHA/x8fHIyMhAUVFRnV1xuXpNyaoxtysQCFgXglZWVowgpLe3N+NCJT8/X64eCw1RVlbGurlq37495wmnefPmjOPy4sWL9Qae5s6d2+CgEwDOxK21g0o+Pj6sJ2sRERFKCzw9ePCA1cB4e3uz8gpMmjQJ+/btk3m4oYaGBkaPHi16raOjgzZt2rCCb7UTI6uqqsLJyYkReBLvqn/p0iXG65YtW+LZs2esBNPW1taMbcXHx6OqqqrOHmRjx45lNLB6enpwcnJibFMRQ97U1dXB5/Nx8uRJxMbGIj09Ha9evUJ5ebnE4WCNcVF4+fJl1vkvKCiIdd6fNGkS/v77b8Y+ERcXV+fFnqqqKqs3J9cYfnmHs4wePRq6urr49ddfpVrH2bNnoampKVXvhEmTJjEudiwtLdG6dWvGDUt9+6mmpiYmTZrEKNPV1UVQUBCWL18uKhMKhbh8+bLo4ZWTkxOrN1dSUhL8/PwY5z1fX1+cPHkSAoEAN2/ehEAggKqqqlLyOwHyHSu1bxxriOfGatGiBUaMGFFnks7GpKenhy+++IJRVt9+m5GRwToftW/fHjExMazP6ejoMIIgFy9ebLTAk5mZGby9vXHq1ClRWVhYmCjwlJ+fz9p/pEkh4eXlJQo61f7chg0bGPlDa+8PcXFxrOGM1tbWrF7C4jcGfD4fV65cwbBhw+qtVw1FHZvvC0W1mcpqtzQ0NLB48WKF5OOry7Rp0xivtbS08Nlnn7GGWl2/fl0UeLp48SLjvXbt2iElJYUVVLeysmKcA8Q/p6mpiYqKChw7dgz//vsvHjx4gNzcXFRUVEgcxtvQNl88r1BGRgaCg4PRo0cPtG3bltGmmZmZwczMrEHbk8Unn3wiCjoBb/cBNzc3RuCpJn9WXT2puTTV/RnwNmgzZ86cepf7/vvvJSbf5/K+tCnSaMi1oJaWFs6dO4fo6GjcvXsXL1++RFlZmcRhea9evUJpaWm9sYvFixcr5IFcXRoUeFJXV8eoUaNEPTLEd9j3rVEaOHAgBg4cCOB/SRXFI7QpKSlYv349Fi9erLDt1nXBLz4ErTFxRXvFAw+RkZFYtGiRTDP9yZuLoKm2m5CQwIrSi8+64OPjg61btzLKIiIiGjXwlJWVxQqKxMTEcJ6QxT18+LDO99XV1RlDAeQlFApZQTwjIyN069ZN9Lp169ZwcHBgNLznz5+X6qQpD/FGDHh7kyvO1NQUFhYWnMvXxcrKinXciA9r1NDQYA0lFZ8lraKigvFa/GIsMzNTqoa+ppt1XQk7uXpE1VcfeeTk5GDSpEmc+askkWe4o6y4Lr64ersaGxvD2tqasXx9F24WFhasnlhcF16Sxv1Lw8/PD97e3qIElYmJiaxzWG2RkZGYOXMm4wKYi/jwYaD+/UL897C2toaxsTFrPVzHXO2u7jV5nmrfyNy4cQN+fn6MYXf9+vVDWloa0tPTUVxcjAcPHqB58+as3tiKCjzJc6yIn0OaNWvGOXz6Xeph3ZTwxhYAACAASURBVKFDB9b5t779lutYCAkJYTyJlaS+NknRxo4dywg8Xb58GTk5OTA3N0dsbCwjoGBlZSXV/iM+qxvwdj+2t7dnBJ7evHmDkpIS6Onpcf5mtZ+I10XW30xRx2ZjGTNmjMTfnWu4tThFtZnKardcXFwU3qNCnIaGBmd6D67hTjXDPUtLS1ntR83EJ/V59uwZKisrRTez9+/fx9SpU+vMiyeuoW2+lZUVunXrJgrw8ng80TGloqICKysrODg4oHv37vD19W1Qon5ZSdN+APJdbzXV/Zk0tLS0MG/ePJl7VzVmm8J1Lqzdk7mh5L0WLC4uxvTp06XKASj+ubruoczNzeWeUVkWDQo8AW+frm7fvp0V5dfX1693CvB3jb29PavL2KeffgpfX1/GbDrHjx/HvHnzFJZzpq5uauJD0BoTVzK22jcmGRkZmD9/vsw9QORNbNtU2+XqoSM+E5yTkxOsra0Z46VjYmJYsyopU0Ma5/oSbRobGyskCn7jxg3WBYenpydrSmkfHx/GRV1FRQXOnj1b79Ncridm9e0nXENZJF38mZqayhx44lqX+Pc1NjZm9Sasb/a52uckWdX39+a68Kpv2m95zJ07V6aLd6Dh5w9pcP22de0TtS+G6vu7cP22XF3oG5rgUUtLSzSsFnh7A5CQkICIiAjOpJnXr1+vN/Akz34h/nvU9TuKEz+nubu7MwJPNTc+tXujdOnSBV26dBElbL1x4wZr+JGi8jsB8v0m4uccrgtcgPs3aSrSfs/a+60y2yRF6969O2xtbUUByurqaoSHh2PKlCms2eyGDBki1eygkvZ1rvLS0lLo6ek16m+myGOzMXTs2FHi9TLXcGtximozldVuWVlZyVwvWRkZGXHuu5L2SaBhvxvw9rczNzcHj8fDjBkzZAo6AYpp89etW4c5c+bg+vXrjHKhUCia1e78+fNYt24d5s6di6CgoAZvUxrynFel0VT3Z5JoaGhAX18fdnZ26NGjB0aNGsU5w119GvP82KZNG1aZeMqLhpD3WnDFihUyB50A1JukvDHOP4ACAk+Wlpbo168fa2asoUOHypzM9V1kbm6Orl27MqbbLSsrQ1ZWFmxtbZuuYkpWVVWF06dPs8prdwMMDQ1lndTatm2L4cOHw8LCQnQASfNESRaNud2qqipWEjvgbc6F+pSVleHcuXNST7HeULLODFFbfSckRQVZuYJ4YWFhnAkHxUVERNQbeOL6Hi9fvqzzM1xPALh6+wHyDS+TZly+rGP3gbdDX+S9Oavv781VH2lutGTx+PFjVuOppaWFkSNHMnpXiCdTbQxcE2Lk5+ejdevWrHLxfaK+yTQa47fl0qpVK7Rq1QqjRo3C8uXLWTMOSdrna+M6D9RXd/H9VNITQ65jS/ycJt7b4f79+3j8+LEo4G9lZQULCwu4ubmJZodKSkpC8+bNGZ9TVH4nQL6/p/g5R9Jx3FgzOEpDnu+pzDZJGcaMGcNI0B0WFoZx48axArXSztQsaV/nKq/ZJxrzN1Pksfk+UESbqcx2S57rAFkVFBRAKBSyjt269smGThBVc70eHx/P6rFiaGiIkSNHws7OTnTPqIyRHubm5jhw4ABSUlJw8eJF3L17F0+fPsWzZ88YvYlqekN16dJFoYmkJVHW9UBT3Z/VsLa2lmrGbFk15vmRa+KVy5cv482bNxIfFslCnr99SUkJK+ewqqoqhg4dio4dO4p6THElAZenPsqgkEfYY8aMYe1gikhO9q7gijQ3VnfEprJ582bWuFIbGxvGU2LxHDWampoICQlhdBOVtXeINBpzu//++2+D/tYnT55sUOBJlgbI2tpalHugxrRp0zB79my5t69IfD5f5hNhbVevXkV+fj7jyZz4jTDXRWV93cG5nmqkpaWxyvLz8+sNYjWmtm3b4ubNm6LXbm5uOHToUBPWSLb9lWt61wULFmDcuHGMMmmml1Z04Iar509qaiqrl8ybN29YT2/r6zWkTC9fvoSenl69Nwq9evViBZ50dHSUUifx/TQ7OxsFBQWs4afiM8EB7GNTPM9TdXU1I+G/m5sb47/A2+NfPGeHoobZyat169aM2ZPKy8uRmZnJ2ne4fpP3Cde59ZdffkFAQEAT1KZ+w4cPx/r160VDUx4/fowNGzYwbuCcnJzqHKZcG9ffr7q6mtVbxtjYWHTMcv1mZ86c4SxvKEUem+8DRbSZimy35NHQto7H4yE9PZ01pKb2rHY1ah606Orqonnz5ozhdkOGDGHN5lwfrt9u7dq1rJQUtSdZkETe38HV1ZWRTF4gEODatWuYPn06o2dXbGxsowSelKWp7s+UrTHbFHNzc7i7uzMCzZWVlVi7dq1UM4iWlZUp/LoqMzOTFVCcOHEiY0IIAIwZgBVJEdfaCslg16dPH8Y4RTc3N1ZCxfdVTk4OK6kkgEZNPteYqqqqsH79emzfvp313vTp0xlJD8Wjx2pqaqynyOIzskkiPhytrie9itxufbh66Mji0qVLrBk1ZME1TE/Sb6Orq8u44QLeTjsqPi1zbYWFhThw4ADn0BtFi4uLk6pXhSR8Pp+RgwNgH4epqamMrrj5+fk4cOBAneu1s7NjdTM/c+YMa1KEXbt2ydxtWZn69OnDeH3z5s0683lVVVUhJiYGR44cUVqduHqRSHqKztWlW7yRvnr1qlR5JGTZrjR69erFamD//vtv1gxQu3fvZs1OqIhcaPK6ceMG+vfvj02bNtU5nKF2D94ayurBK/571EztW1tZWRn+/vtvRpmKigp69erFKFNXV2ed48LDw0X/X/OetbW1qBt/dnY2q3t8UweeuLYvPmPm69evERoaqrBtytLGKoqtrS0rh8WuXbvqPDZfv36NHTt2iIZKNiYDAwMMHjyYUSYeoJW2txPwNjeheJApPDycMWkEAEaOw969e7POPb///rvExMvA255/K1askLpetbdVW0OOzfeBItpMRbZb8hBv6968eSPz8Kht27YxXldWVnJOYlA7/5D4bxcTE4Nbt25J3EZZWRn++ecfxjUbV48T8d8uNDQUL168qPsLgLvNl3RO4/P5CA8P58ybqKqqiu7du7OGY79LvU3l0Zj3SY2psduUWbNmscqOHz+OtWvXsq79arx48QKLFy+WexbsunAd6+Kjyx4+fMgaHq4oshx3kiikx5OqqioOHTokuuFTRBe0pvDgwQPRzCE8Hk80/aP4+OZWrVrJPU33uyQvLw+RkZEQCAQoKirC/fv3ERsby5mEdtiwYaJcITVsbGwYs4SUl5cjKCgIw4YNA4/HQ3R0tNRdjS0tLRmvHz16hOXLl8PFxUXU/a9///5o1qyZQrdbl5KSEtYNmo2NTZ0zVoknuKypT+1ZzWTBNQZ648aNyM/PFx1nrVq1Ej3BmTJlCiM6n5eXh6FDh8LPzw/t27eHvr4+SkpK8PTpU9y5cwc3b94En8+XOnlpQ3AF8X744QeJQVw+n48ffviB0YCePHmSMUWqi4sL429dUFCACRMmIDAwECUlJThw4EC9J0VVVVUEBAQwgq1VVVUYN24cxo8fj+bNmyM+Pp5xg/suGDduHHbt2iUKhggEAsyYMQP9+vWDu7s7TExMUFVVhefPnyMtLQ0JCQkoKyvD8OHDldYjVfw4BoDly5dj5MiRoi7Sjo6OaNeuHWxsbFjLrly5EtnZ2TA3N0dqaiqOHj1a5w2XPNuVRqtWrTBgwABGD70nT55g9OjRCAgIgL6+Pq5cucLap7W1tTF27FiptqEsBQUF2Lx5M/7880+4uLjAzc0NrVq1gq6uLt68eYMLFy4gPj6e8RkjIyNWQEdRxo0bh927dzOCdjt27MDz58/Rs2dPlJaWcgbIBwwYwJiqu4a7uzvj/F/74q928nM3NzfRRAa1L9YUmd9JXkOGDMG6desYv8mRI0dQXFyMPn36oKCgAPv3729wbpXaLC0tGcNXrly5gvXr18PW1hbq6upQUVFRyrDwyZMn46effhK9zsjIwODBgzFkyBDY2tpCR0cHxcXFyMzMxM2bN5GamgqhUIiOHTsqvC7SGDt2LCPgV7vHu7q6uky5S6urqzFhwgR89tlnsLa2RmpqKmcPm9rXBy1btoSvry9jFrvTp08jPT0dvr6+sLS0hJqaGt68eYOHDx/i2rVrop4LixYtkum7KvrYfNcpos1UZLslD/G2rqysDPPmzUOvXr1EN4UeHh51Jik/deoUysvL4e3tjcrKShw/fpw1+UKnTp0YSci//PJL/PPPP6LvVV5ejsDAQAwcOBAuLi4wNjZGeXk5srKycPfuXVy7dg1VVVX45ptvROvgergxb948BAYGwtDQENeuXcOJEyfk+h1q1jVo0CBRMKtTp06wtrZGdXU15s2bh6VLl8Ld3R3t27eHlZUVtLW1UVxcjHPnzrG+v/jw7PdNY90nNYXGbFO6du2KyZMnY+fOnYzyv/76C6dOncKgQYNgY2MDVVVVUUeV+Ph48Pl8xr6vKG3atIGqqirj/LJz505UVlbCxsYGjx49wsGDB2VKKC8LWY47SRSWLbZFixasiPH7RtoxkcrYmZrCo0ePpBrf6+/vj2XLlrHKhw8fjn379jEuzJKTk5GcnCx6LT47mSTu7u6snhjiTxr//fdfWFhYKHS7dTl79izrCcknn3xSZzJ4Nzc3HDlyhFG3kydPyh14atGiBdq2bcsYF5+RkcGY2njUqFGiwFOfPn0wYcIExtOrsrIyHD16VK7tK0p5eTlrOG6rVq3w+eef1/m5sLAwRm+smzdvIisrSxT4HTZsGIKDgxkn4Xv37jFmndTU1JT4ZKLGlClTcOLECcaTtpycHKxbt070Wk1NDfr6+o2e9FYSExMTrFy5Et9++61ofxMIBDh//jzOnz/fJHX66KOPoK+vz+h1Jj7l8qxZs9CuXTu0b98eH330EaOLf0FBATZu3Ch6raamxkj4q4jtSmvJkiVISUlh7BMPHjyos4v10qVL35nesAKBALdu3arzqXSNWbNmKW0KXVNTU/z888+sruARERGsXAU1LCwssGTJEs73JPVW0tPTY/S0rh14qk2R+Z3kZWhoiFmzZrEC/lFRUYw6m5qaKuzJu7u7O+Li4kSv+Xw+o+eDmpqaUgJPo0ePxqVLlxg9SwoKCjh7WLwLXF1d4eTkxDm8rFevXjIlfDczM0Nubi7jnCauf//+rN4kixcvxu3btxkBn4yMDGzZskXqbUtD0cfmu04RbaYi2y15cJ3/xP9eBw4ckBh40tLSgkAgwIULFzh7vgJvA6ziM3e3a9cO8+bNw6pVq0RlPB6vzn1FXO/evVlD9p4/f84YWqejowNjY2PGRD1cunXrxroBj4uLY5zj1qxZw7gBLisrq/N711BTU8OgQYOk+k7vqsa6T2oKjd2mzJkzB8XFxazJC7Kzs1k9RJXNyMgIXl5ejO9eUVGBHTt2MJazt7fnHNraUPIcd+IUMtTuQ6GhoYF58+bVm+D4v8LKygrLly/H2rVrOS/UP/roozoDV1ZWVlJfKA0YMIBz6mEuitxuXbga0/oaI0tLS9a48OvXrzOmTpbVrFmzZBpX+8MPP2D27NlSJ4ozNDSUa3YJWcTGxrKGKQ0YMKDez4kvIxQKGU+CHR0dMX36dImfNzc3x4YNG+rdjp6eHnbs2CExaKChoYGff/4Z9vb2jPLaQ0+bwqBBg7B161app2DW0NBQ6qQIWlpamDZtmlTLqqioYM2aNRJ7yKqpqWHJkiVSPaWSZbvSat68OQ4cOMDIByGJjo4OfvvttyZvG4yNjWXKKaChoYE5c+aw8pMomr+/P9avXy9VklpnZ2fs379f4hNnZ2dnzu/YqVMnxvFYu/dTbU09zK5GUFAQo/emOCcnJ8ZT3RrynnM+/fRTpZ/nuaiqqmL9+vWYMGGC1O1Y8+bNm7TnvKRei7IMswOAmTNnwtPTU+L7nTt3xpo1a1jlJiYm2L9/P2dyW0nkTW+hyGPzfdDQNlOR7ZY8OnfujI8//ljuz5uZmWHNmjUSJ4zR0NDA6tWrOfMbffHFF1ixYoXUbYy2tjajZ1yzZs3w22+/SZx4qub9um5aa5ibm2PMmDFS1UMW6urq+PHHH9/7dDGNdZ/UFBq7TVFVVcXPP/+MtWvXSrVvAm/3I2W1tz/99FOdPU6nTZuGgQMHKmXbijjuFD8/9n+IlpYWDAwMYGNjg+7du2Po0KHvZffiuqioqEBdXR26urqiAES7du3g4eEBLy8vqKmp1fn5KVOmwNHREXv27MHt27fB4/FgaWkJLy8vTJkyReoDXVNTE3v37sX27dtx4cIFZGVl1dlVUFHblSQvL4+V98jOzk6qHhMDBgxg9LQQCASIjIzExIkT5aqLr68vDA0N8ffff+P27dsoLCysc0y/qqoqpk2bhuHDh+Po0aNISEjA48ePUVhYCBUVFRgaGqJNmzZwcnKCh4cHevXqpbBZ6ySRJ4gHAD4+Pli+fDkjuh4REYGpU6eKXs+YMQN2dnbYv38/0tLSUF1dLRoqNXHiRKl7KDk4OODkyZPYtm0bYmJi8OrVKxgZGaFr166YNGkSnJ2dWbkuaidqbCqenp44d+4cwsPDcfHiRdy7dw9v3rwBj8eDnp4erK2t4ejoiO7du6Nfv35Kv6GbNGkSrKyscPjwYaSnp6OoqEjisAN7e3uEhYVh69atuHDhAvLz82FkZIROnTohKCgIXbt2larHjqzblZa1tTVCQkJw7tw5REVFISUlBbm5ueDxeDA0NISdnR169+6N0aNHsxLyNoWePXsiISEBiYmJuH79OlJTU/Hs2TPk5uaivLwcqqqqojata9euGDFiBOdMfcrg6+uLXr164ejRo4iLi8ODBw9QWFgIDQ0NmJiYoGPHjhg8eDD69+9fZ3ClJs9T7SdsAFhDBR0dHRmJyGu8K4EnAPjxxx/Rs2dP7N27F3fv3gWfz0fr1q3h7++Pzz77jHNWIHn3MyMjI4SEhGD79u2Ii4vDy5cvOXOeKIOmpiYWL16M8ePH49ixY7h+/TqePHmC4uJiqKqqwsjICDY2NnB1dUXPnj3h7u5e77WHMvn5+WH16tWMHpS6urro37+/TOvR0NDA1q1bcezYMRw/fhwPHz4En8+HjY0Nhg4divHjx3NOnQ68vcAPDg5GQkICIiMjkZycjJycHJSUlEBLSwvNmzeHra0t3Nzc0LdvX1ayaFko6th8XzS0zVRkuyWPTZs2Yc+ePYiOjkZGRgbroV59fH194ejoiJ07dyI+Ph65ubkwNDREjx49RNfWkowaNQre3t4IDQ3FlStXkJ6ejsLCQggEAujr66N169Zo3749evTogb59+7Jm8OzRoweOHTuGrVu34urVqygqKoKJiQm6d++OyZMnw8HBAcHBwVJ9jyVLlsDe3h7h4eF4+PAhSktLOSeD0tLSwpkzZ3Dt2jXcuHEDaWlpeP78OUpKSqCiogI9PT20adMG3bp1Q0BAwHuZOJ+Lsu+TmlJTtCn+/v7w9fXF2bNncfnyZdy6dQu5ubkoKiqChoYGTE1NYW9vDw8PDwwaNAjm5uYK+rZMLVq0wLFjx7B9+3acPXtWNKmMk5MTxo8fD09PT2zatEkp2wakP+4kURHKsjQhhHygcnNz0bdvX0bQz9fXF+vXr2/CWhFC/quWL1/OGnIeFRUl9axqRH4zZ85kpF4YMWJEnbkQs7KyWIGplStXYsSIEUqrIyGEEPI+ef8fXRBCiAKcOHFC4phoPp+PFStWsHqavY8z+xBCml5eXh4OHjwoMf/crVu3cOzYMUaZlZUVBZ0aQWlpKRISEhhlsg6zI4QQQggTDbUjhBC8neXp+++/h5ubGzw8PNCyZUuoqKjg6dOnOHXqFDIyMhjLW1hYYMiQIU1UW0LI+6y8vBw///wz/vjjD3h5ecHJyQkGBgYoLi5GUlISoqOjwePxGJ/58ssvm6i2/323bt1CVlYWCgoK8M8//zCGaNekWyCEEEKI/CjwRAghtSQlJSEpKanOZTQ1NfHbb781+exYhJD3W0FBAUJDQxEaGlrncp6enhKTXpOGO3ToEMLCwjjfmzlzpkwTfBBCCCGEjYbaEUKIDFq2bIng4GB069atqatCCPmPU1FRwYQJE/DHH380acLtD9XQoUPh6+vb1NUghBBC3nvU44kQQgDMnj0bnTt3RlxcHB49eoS8vDyUlJRAW1sbZmZmcHZ2hpeXF3x8fKChodHU1SWEvMcsLS0RHByMS5cu4caNG3j16hXy8/MhEAhgaGgIGxsbdOnSpVFnHiRv1fz+Y8eOhb+/f1NXhxBCCPlPoFntCCGEEEIIIYQQQohS0FA7QgghhBBCCCGEEKIUFHgihBBCCCGEEEIIIUpBgSdCCCGEEEIIIYQQohQUeCKEEEIIIYQQQgghSkGBJ0IIIYQQQgghhBCiFBR4IoQQQgghhBBCCCFKQYEnQgghhBBCCCGEEKIUFHgihBBCCCGEEEIIIUpBgSdCCCGEEEIIIYQQohQUeCKEEEIIIYQQQgghSkGBJ0IIIYQQQgghhBCiFBR4IoQQQgghhBBCCCFKQYEnQgghhBBCCCGEEKIUFHgihBBCCCGEEEIIIUpBgSdCCCGEEEIIIYQQohQUeCKEEEIIIYQQQgghSkGBJ0IIIYQQQgghhBCiFBR4IoQQQgghhBBCCCFKQYEnQgghhBBCCCGEEKIUFHgihBBCCCGEEEIIIUpBgSdCCCGEEEIIIYQQohQUeCKEEEIIIYQQQgghSkGBJ0IIIYQQQgghhBCiFBR4IoQQQgghhBBCCCFKQYEnQgghhBBCCCGEEKIUFHgihBBCCCGEEEIIIUpBgSdCCCGEEEIIIYQQohQUeCKEEEIIIYQQQgghSkGBJ0IIIYQQQgghhBCiFBR4IoQQQgghhBBCCCFKQYEnQgghhBBCCCGEEKIUFHgihBBCCCGEEEIIIUpBgSdCCCGEEEIIIYQQohQUeCKEEEIIIYQQQgghSkGBJ0IIIYQQQgghhBCiFBR4IoSQD4iXlxcWLFjQpHWYMGECJkyYwCgrLy/H0qVL0adPHzg6OmL69OkAAEdHR2zatEmu7Tg6OmLRokX1LpeVlQVHR0eEhobKtR1CCCGEEEKIZBR4IoSQRrJp0yY4Ojri5cuXnO8vWrQIjo6OjVyrd8Nff/2Fw4cPY/jw4VizZg2CgoKaukoNsmDBAjg6Oor+tW/fHr1798bUqVORlJSk9O1nZWVh06ZNSEtLU/q2CCGEEEIIqYt6U1eAEELIh2XXrl2ssitXrsDR0RFz5sxhlKekpEBNTa2xqqZQqqqqWLVqFQBAIBDgxYsXOHr0KCZMmIBDhw7B1dVVadvOzs7G5s2b0bp1a7Rv315p2yGEEEIIIaQ+FHgihBDSqDQ1NVll+fn5aNGiBatcS0urMaqkFCoqKhg6dCijzMfHB5988gmioqKUGngihBBCCCHkXUFD7Qgh5B1Wk6fo33//xbBhw+Di4gIfHx+cPHmStSyPx8OOHTvg5+cHFxcX9OjRA1988QWuX78ucf1VVVXYuHEjRo0aBXd3d7i6umLo0KGc+Y7u3buHqVOnomfPnnBxcUGfPn0wY8YMxtBBaZapneMpISEBjo6OyMzMRGJiomhoWkJCguj7187xVFBQgNWrV8Pf3x9ubm7o1KkTxowZg/Pnz0v8jmfOnMGQIUPg4uKCQYMG4cSJE3X84v+Tm5uLH3/8Eb1794azszN8fHywfft2CAQCqT7PxczMDABYvbiEQiEOHjwIf39/uLi4oHv37pg9ezays7MZy12/fh2zZ8+Gl5cXnJ2d4eHhgblz5zJ+39DQUHz22WcAgHnz5ol+05q/6bNnzzBnzhz06dMHzs7O6NWrFyZPnoz09HS5vxchhBBCCCGSUI8nQgh5x92+fRvnz5/HmDFjMGrUKBw9ehTz5s3DRx99BDs7OwBvh3J9/fXX+Pfff+Hl5YVPP/0UfD4fycnJuHbtGrp27cq57pKSEhw+fBi+vr4YPnw4eDweYmNjsXDhQvD5fIwePRrA2x5JQUFBMDQ0RFBQEIyNjfHq1SvExcUhJycHFhYWUi0jzs7ODmvWrMGaNWtgYGCAadOmicq5PHv2DNHR0Rg4cCBat26NsrIynDx5El999RX++usv9O7dm7F8SkoKoqOjERgYCGNjY4SHh+P777+Huro6fH19Jf7m+fn5GD16NKqqqjB69Gi0aNECSUlJ+P3335GdnY1ly5bV/4f7//UAbwNLL1++xNatW6GpqQk/Pz/GcsuXL8fhw4cxZMgQjB07Fnl5edi/fz/Gjh2Lf/75ByYmJgCA06dPo6CgAKNGjYKZmRkyMzNx5MgRpKSk4MSJE2jWrBm6deuGadOmYdu2bRg7diw6d+4MAHBzcwOPx8PEiRNRXl6OMWPGwMLCAnl5eUhMTERGRsYHm2OMEEIIIYQokZAQQkij2Lhxo9DBwUH44sULzvd/+OEHoYODA6PMwcFB2KFDB2F6erqo7PXr10InJyfhqlWrRGVhYWFCBwcH4caNG1nrFQgEov/39PQUzp8/X/Saz+cLKysrWZ/5/PPPhT4+PqLXZ8+eFTo4OAhv3bol8ftJs4xQKBSOHz9eOH78eEaZt7c3q0woFLK+U2VlpZDP5zOWqaysFA4ePFgYFBTE+qyDg4Pw6tWrorKysjKhj4+PsG/fvqL1PHv2TOjg4CA8fvy4aLklS5YI3d3dhTk5OYx1rlu3Tujo6Ch8/Phxnd9x/vz5ou3X/telSxfhhQsXGMsmJSUJHRwchEeOHGGU379/X+jk5CT8/fffGfUXd+3aNaGDg4MwPDxcVBYfHy90cHAQ/vPPP4xl7927J3RwcBBGRUXVWX9CCCGEEEIUhYbaEULIO65r165wcHAQvTYzM4OtrS2ysrJEZadPn4aenh6mTJnC+ryKiorEdaupqYlyLvF4PBQUFCA/z54zFgAAIABJREFUPx8eHh548uQJiouLAQAGBgYAgHPnzqGqqopzXdIs01CampqiYWpVVVV48+YNSkpK0K1bN6SmprKWd3R0RI8ePUSvtbW1MXr0aLx8+RL37t3j3IZQKMTp06fRr18/qKurIz8/X/Svd+/eEAqFoqGAdVFVVcWePXuwZ88e7N69G7/++ivatm2Lb7/9ljGzXVRUFJo1awZPT0/GtkxNTWFjY4P4+HhG/WuUlJQgPz8ftra2MDAw4Pz+4vT09AAAly5dQmlpab3LE0IIIYQQ0lA01I4QQt5x1tbWrDJDQ0MUFBSIXj958gRt2rSRKxl3WFgYdu/ejYcPH7LyFxUXF0NfXx/dunWDn58ftm7dir///htubm74+OOP8cknn8DY2BgApFqmoYRCIfbs2YMjR44gMzOT8R5XgM3GxkZiWVZWFpydnVnv5+fno7CwEOHh4QgPD+esR15eXr11VVFRQc+ePRllgwYNwsCBA/HLL7+Ici5lZmaioqKCNUywRqtWrUT/n5OTg7Vr1+LChQuioGCNoqKieuvUsmVLTJo0CX/99RdOnDiBzp07o0+fPvD39+ccCkkIIYQQQkhDUeCJEEIaSU1QqKKigvP9iooKNGvWjFWuqqq8zqlRUVFYsGABPv74Y3zxxRcwNTWFhoYG/v33XwQHB4sCUSoqKvj999/x5Zdf4sKFC7h69SpWrlyJLVu2YO/evXBwcJBqmYbauXMn1q1bh6FDh+Lrr7+GsbEx1NTUcPz4cURERDR4/QBE39nX1xcBAQGcy9QOBslCV1cXrq6uiI2NRVlZGXR0dCAQCGBgYIA//viD8zM1+41AIMDEiRORm5uLL7/8Eu3atYOOjg5UVFQwe/ZsCIVCqerw/fffY+TIkTh37hyuXr2KTZs2YevWrfjzzz/h4eEh1/cihBBCCCFEEgo8EUJII6npufT48WO0bduW9f6jR49gZWUl17rbtGmDxMREVFZWytTrKTIyEi1btsS2bdsYPYZqD++qzcnJCU5OTvj666+RlpaGkSNHYs+ePVi5cqVMy8grKioK7u7uWLNmDaP8+PHjnMtnZGRILGvZsiXnZ0xMTKCnpwcej8fqsaQI1dXVACAKPLVp0waXL1+Gi4sL9PX1JX7u/v37ePjwIVatWoXhw4eLyisqKli9neoaXgkAtra2sLW1xaRJk/DixQsMGzaMAk+EEEIIIUQpKMcTIYQ0Eg8PD2hqauLgwYPg8/mM9xITE3Hv3j18/PHHcq170KBBKC0txY4dO1jv1dUTpqY3Ve0hdm/evGEFcgoLC1nrsbOzg5aWlijoIc0yDaWqqsraRmZmJs6ePcu5fHp6OiOIVl5ejpCQEJibm6NDhw6cn1FTU8OgQYNw7tw5pKSksN4vKSmRO4dVQUEBkpOT0bx5c5iamgJ427NKKBRK7PFUMzNeTTBJfDjk7t27WWU1uaDEh+OVlJSw9j1LS0uYmJiwliWEEEIIIUQRqMcTIYQ0EhMTE3z77bdYs2YNRo8ejQEDBsDQ0BD379/H0aNHYW1tjcmTJ8u1bn9/f5w8eRKbN2/GvXv34OHhgerqaiQnJ6NDhw6YNm0a5+f69++PM2fOYOrUqfD29kZeXh5CQkLQokUL5ObmipYLCwvD/v374ePjg9atW6O6uhqRkZEoLS2Fn5+f1Ms0lJeXFzZt2oTvvvsO3bt3R3Z2Ng4dOgRbW1vOZOEODg745ptvEBgYCGNjY5w4cQJPnjzBb7/9JkpSzuW7775DYmIiAgMDMXLkSDg6OqKsrAwPHz7E6dOncfLkSYk9pmoIhUJRjiihUIhXr17h2LFjKCwsxC+//CIKJHXt2hUTJkzAvn37cP/+ffTt2xe6urrIyspCbGws/Pz8MGPGDNjZ2aFt27ZYvXo1Xrx4AVNTUyQmJiI5ORlGRkaMbdvZ2aFZs2Y4ePAgtLS0oKOjA1dXV6Snp2Pp0qUYOHAg2rZtC3V1dVy4cAGPHz/G3LlzZf1zEEIIIYQQUi8KPBFCSCP68ssv0bJlS+zbtw87duxAVVUVWrRogdGjR+Prr7+GiYmJXOtVVVXF1q1bsXPnTpw4cQIXL16Enp4enJyc0K1bN4mfGzZsGAoKCnDgwAGsWLECVlZWmDhxIvT19bFw4ULRcu7u7rhz5w6io6ORm5sLbW1ttGvXDlu2bIG3t7fUyzTU1KlTwePxEB4ejjNnzqBt27ZYsmQJMjMzOQNPrq6umDFjBjZt2oTMzExYWVlh9erVGDJkSJ3bMTExQUhICLZu3YrY2FgcO3YM+vr6aNOmDb7++ms0b9683roKBALMmzdP9FpHRwcdOnTA3LlzMWDAAMayixcvhrOzMw4ePIgtW7YAACwsLODh4YHBgwcDANTV1bF9+3b8+uuvCA4OhlAoRLdu3bB3714EBQUx1qejo4NVq1Zh48aNWLp0Kfh8PlauXIlu3brh448/RlxcHI4fPw41NTW0bdsWv/76K0aOHFnvdyKEEEIIIURWKkJps5ESQgghhBBCCCGEECIDyvFECCGEEEIIIYQQQpSCAk+EEEIIIYQQQgghRCko8EQIIYQQQgghhBBClIKSixNCCCHkgyUUClFZXYmq6ioIBAJABVCBSsPXi7cpNNVV1aGppglNNc0Gr5MQQggh5H1EgSdCCCGE/GfwqnmorK4EX8AHAKipqEFTTRPqquooqSpBYWUh8svz8ar0FV4Uv0B2cTZelb5CblkucstykVeeh3JeeYPqoKKiAl0NXZjpmIn+WehZoKVBS5jrmqO5bnOYaJvAUMsQ2hraqORXgifgQSAUQAUqUFdVRzP1ZlBTVVPET0IIIYQQ0qQo8EQIIYSQ94pAIEAprxQCoQA6GjoorirGw/yHuPnyJh7kPxAFkWr+vS59jaLKIlEvpHeJmooajLWNGUEqMx0zNNdpDucWznAxd4GNkQ3UVdVRwa+AhqoGdDV1m7rahBBCCCFSUxEKhe/eVRghhBBCPni8ah7KeGVQVVFFM/VmyCnNwb3X95D0Igl3Xt/Bvdf3kJ6XjpKqkqauqtKZ6ZihvVl7dDDrAFdzV7hZusHexB5GzYze9tBSAXQ1dKmXFCGEEELeORR4IoQQQkiTEggEKK4qFuVByizIxJ3Xd3Dj+Q3cy72HtNw0PH7zWDR8jvyPtro2HEwd0N6sPT5q/hG6WnVFB7MOsDawRlV1FYRCIfS19Ju6moQQQgj5gFHgiRBCCCGNii/go7SqFNoa2sh4k4Ezj84gJiMG17Kv4UXJi6au3n+CqooqbIxs4NHKAwNsB8DLxgtmOmao4FdAX0sfqio0sTEhhBBCGgcFngghhBCiVJX8SlTwK9BMvRlSX6fi9MPTOJ95HlefXUUpr7Spq/fBaKHbAr1b94a3jTe8bb3RxqgNynnl0NPUoyF6hBBCCFEaCjwRQgghRKHKeGXgC/hQV1XHjec3EPkgEhefXMSNFzdQVV3V1NUj/09fUx89W/WEl40XBrUbBEdTR1TwK6CtoS0a9kgIIYQQ0lAUeCKEEEJIg5TzyiEQCsAX8PF/7N15nM1l/8fx15kz+8bYlyJLxpYt+5otRVHWoonKEkk3kZvq16aQtEkqWZIlOylCSt2RECFb2Zcwg8GcM8uZmXN+f5zb3E22wcxcZ855Px+PeeB7lu975nf3G/N2XZ9r/dH1LN+3nP8c/g87YnfgdDlNx5MsCrIGUbdkXZqVbkbb29tSo1gNUp2phPiHEGANMB1PRERE8igVTyIiInLdbCk2/K3+/HnmT2Zun8nSvUvZe2av6ViSjawWK3VK1qFTpU50q9KNAiEF8LP4ERIQYjqaiIiI5CEqnkRERCRLziefJ8g/iA3HNjBz+0yW/bGMWHus6ViSSyoUrMCDFR+kxx09uL3g7aQ50wgPDDcdS0RERDyciicRERG5LJfLRUJKAv5Wf1bsW8Gs7bNYtX+VBoILRcKKEFMthjdavkFgqhPS0iBcJZSIiIhcyt90ABEREfEsF1IuEOAXwMr9K5m6dSor96/UUHDJJNYeS6w9lpTYEwRWqQkPPAB9+kDNmiqhREREJBOteBIREREupFwg0BrIdwe/Y8rWKSz/cznJacmmY4kH++7RNTT/Zi8MGPC/i4UKQceO7hKqalVIT4ewMHMhRURExDgVTyIiIj4qNT2VVGcqv8f+zjsb3mHZ3mXaRidZEh4YTtzQOILLVYCjRy//pKJFoVs3GDIEChaE0FDw88vdoCIiImKciicREREfY3PYsGDh8+2f8+6Gd3UanVy3blW68UnTcUQWLZW1FzRtCsOGQatW4HJBiE7GExER8RUqnkRERHyA0+kkMS2Rk7aTvLnuTWbtmEViaqLpWJJHLe++nHv/cwKeeOL6XlisGDz5JDz9NPj7Q2RkzgQUERERj6HiSURExIslpiZiwcLyfct5a/1bbDi2wXQkyeNC/EM4+9wZgivdAfv339ibWK3Qvj0MHw533AEBAe4PERER8ToqnkRERLzQhZQLJKcl896G95i8ZTJxiXGmI4mXeLDig0xr9QH5CpXMnjeMjoZnnoFHH3Vvw9OJeCIiIl5FxZOIiIiXcKQ5SHels/mvzYxdN5YV+1bgdDlNxxIvs7jrIh7YmgQ9emTvG4eGQvfu7lVQxYppGLmIiIiXUPEkIiKSx9kddpwuJ1O3TuX9je9zIP6A6UjipQKtgcQPjye0Vj34/fecu1G9ejB0KLRr5y6fgoJy7l4iIiKSo1Q8iYiI5FF2h510Vzqj/zOa9ze+r2HhkuPa3t6W2fdOIV+B4rlzw+LF4dVX3aurrFYIDMyd+4qIiEi2UfEkIiKSxySmJuJ0ORm3bhxvb3gbm8NmOpL4iDkdZ9PtzyAsnTrl7o1vvRVGjYIuXdyn4WkQuYiISJ6h4klERCSPSEpNwuly8u6Gdxm3fhznU86bjiQ+xN/Pn/jn4glv0gI2bTIT4rbb4I03oEMH9+onf38zOURERCTLVDyJiIh4uOS0ZJxOJxM3TWT0T6OJT443HUl8UKuyrVjQYTb58hUxHQXKl4cxY6BtW/fqJxVQIiIiHkvFk4iIiIdKSUsh3ZXO5F8nM+o/ozideNp0JPFh0zpM49G/CuPX7j7TUf6nYkUYNw5atHAPILdaTScSERGRf1DxJCIi4mEcaQ7SXGl8tu0zXln7Cqfsp0xHEh/nZ/Hj7HNnyXdPB/jhB9NxLlW1Krz1FjRpAsHB7pPwRERExCOoeBIREfEQjnQH6c50Zu+Yzf+t/T/+SvjLdCQRAJqUasJXXRYTGVHIdJSrq1kTxo+HunUhJEQFlIiIiAfQd2MRERHDXC4XiamJzNs5j+gPoum9rLdKJ/Eo3e/oTtgvW03HuLatW93b7po3h40bwaYTH0VEREzTiicRERGDbA4bxy4c45FFj/DriV9NxxG5hAULp4fFUaDLo7B8uek416dzZ/j4Y/fqp5AQ02lERER8klY8iYiIGOBId2Bz2Bi5ZiRVPqyi0kk8Vt2SdQnAL++VTgALFsBtt8H06ZCYCE6n6UQiIiI+R8WTiIhILrM77Hz9x9eUf788EzZOwOnSD8PiuR6u+jAhW3aYjnHjEhJgwABo1Ah27tT2OxERkVymrXYiIiK5xO6wE58cT88lPfnu4Hem44hkyclnT1L0sYHu1UN5ncUCffvCuHEQGAhBQaYTiYiIeD2teBIREclhac40ElMTGf/zeMq9X06lk+QZNYrVIMwa7B2lE4DL5Z75VK4cLF0KdrvpRCIiIl7P33QAERERb2Zz2NhyYguPLX2MA/EHTMcRuS4PVXmIwB27TMfIfnFx0K0bNG0KM2ZAwYIQHm46lYiIiFfSiicREZEckJSaxJnEM/Rc0pNm05updJI8qccdPQj88GPTMXLOjz/C7bfDmDHu1U+pqaYTiYiIeB3NeBIREclGTpeT5LRkpm6dyog1I7A5NMhY8qbKhSuz8YkNhAVHmo6SO0qXhilToF49rX4SERHJRtpqJyIikk3sDjtHzh/hoYUPsf3UdtNxRG5K1ypd8d/9h+kYuefwYWjVCtq3h2nTICxMw8dFRESygbbaiYiIZAO7w86nWz6l+kfVVTqJV4i5I4agT6aYjpH7vvwSoqNh/XqwacWiiIjIzdJWOxERkZvgSHdgd9jptqAbqw+sNh1HJFuUL1Cebf1+IzQkEpxO03HMGTwYRo2CkBCwWEynERERyZO04klEROQG2Rw2NhzbQMWJFVU6iVfpUrkLfvv2+3bpBPDOO9CoERw7BklJptOIiIjkSSqeRERErpPL5SIxNZFXf3iVu6bfRaw91nQkkWzVs9qjBE+dYTqGZ/jtN6hYERYvdp98JyIiItdFW+1ERESuQ1JqEmeTztL+i/ZsObHFdByRbFcqXyn2PLWbkPAocDhMx/EsDz0EkydDcDD464weERGRrNCKJxERkSyyO+x8ufdLKk6sqNJJvFanSp1wHTyk0ulyvvgC7rgDdu3S6icREZEsUvEkIiJyDenOdGwOG/2+6sdDCx/C5tBJV+K9elXvSejnc0zH8FyHDsGdd8KHH0Jiouk0IiIiHk9b7URERK7C7rBz6Nwh2n/RngPxB0zHEclRxcOLc2DQfoLzF1KpkhXNm8OCBRAWBkFBptOIiIh4JK14EhERuQK7w87Hv35MzY9rqnQSn9CxUkfSjx9T6ZRV338PFSrATz+BTSshRURELkfFk4iIyD+kO9M5n3yeB+Y+wLOrniXVmWo6kkiu6FW9J2FzFpiOkbecOQOtWsH//Z8KOxERkcvQVjsREZG/SUlLIdYeS/PPmrM/fr/pOCK5plBoIY4OPkpw4eJw7pzpOHlT69awcKF7652f/n1XREQEtOJJREQkQ2JqIttObaP6R9VVOonP6RDdgbSTf6l0uhmrV0ODBhAXp1MBRURE/kvFk4iICGBz2Fi6dymNpzYmPjnedByRXPdY9V6Ez19qOkbet3MnVKsGe/dq652IiAjaaiciIoLdYWfMujGM+nGU6SgiRuQPzs/JZ08SVLIUxMaajuMdgoNh3jxo0cK99U5ERMRHacWTiIj4NLvDzmNLH1PpJD7t/gr34zh9UqVTdkpOhg4dYNIksNtNpxERETFGxZOIiPikNGca8UnxtJzRkvm75puOI2JUr+o9iVi83HQM7+NywbBhMGiQtt2JiIjP0lY7ERHxOclpyZxIOEHzz5pz+Pxh03FEjAoPDCduaBzB5aPhyBHTcbzXXXfB0qUQHq4T70RExKfou56IiPgUu8PO5r82U+PjGiqdRIC2t7fFEX9apVNOW7sW6tSBkychJcV0GhERkVyj4klERHyG3WFnwa4FNP+sORdSLpiOI+IRelZ7lMivV5uO4Rv++MN94t2uXdp6JyIiPkNb7URExCckpibyytpXeHP9m6ajiHiMEP8Qzj53huAq1eHPP03H8R1BQTB7Ntx9t3vrnYiIiBfTiicREfF6doedHot6qHQS+Yc25duQciFepVNuS0mBTp3ggw904p2IiHg9f9MBREREcpLdYafTvE6s3L/SdBQRj/NotRjyrfrBdAzfNWKEu4QaOhTCwkynERERyREqnkRExGupdBK5skBrIHeXawNd65uO4tteftn9q8onERHxUtpqJyIiXkmlk8jVtSzTkrREG/z+u+ko8vLLMG6ctt2JiIhXUvEkIiJeR6WTyLXFVIsh4vv1pmPIRa+8ovJJRES8krbaiYiIV1HpJHJt/n7+3F/hfvz6tDIdRf7ulVfcvw4bpm13IiLiNbTiSUREvIZKJ5GsaVa6GekpSfDLL6ajyD+98gq8+aZWPomIiNfQiicREfEKKp3kRkQGRVIiogTFw4u7f40oTqnIUkQGRRLkH0SAXwAB1oBMv1r9rKQ500hNT8WR7nD/3un+fWp6KnGJcRy9cJQTCSc4YTvBXwl/cSLhBElpSaY/3Qw97uhB+E8bTceQK3n1Vfevzz2nlU8iIpLnqXgSEZE8z+6w03FeR1btX2U6iniI0IBQSucrnVEmlYgoQel8pSmTvwwlI0tSNKwoUSFRACSnJeN0OfGz+BFsDSbQP/Cm75+clkxKWgouXFgtVkICQnCkOzibdJaTtpMcu3CMA/EHMgqqvxL+4q+Evzh8/jBpzrSbvv/V+Fn86FipI9YhD+TofeQmvfoquFwwfLjKJxERydMsLpfLZTqEiIjIjVLpJGEBYdQoVoM7S9xJ01JNqVOyDsXCi5GcmowTJ1aLlSD/IAKtN18oZbeUtBSS05IzCqog/yAOxh9k3dF1rDu6jl//+pWdcTuztYxqXKoxX3dZQmREoWx7T8lBL7wA//63yicREcmzVDyJiEiepdLJ91ypZEpKTSLQGkhIQIjpiNnG5rDhdDoJDgjO1jJqUrtJ9L5wO/4tNVg8z1D5ZEyLFi2oW7cuY8aMMR1FRCTP0lY7ERHJk1Q6eb8AvwDqlKxD7RK1M5VMiamJBFmDMpVMnria6WaFB4Zn/D66UDTRhaLpWqXrZcuoTcc3sSN2xzXf04KFLpW74N+tVw4ml2w3apT7Vw8rnxYtWsSIESOwWq2sWLGC0qVLZ3r8+eefZ8GCBezdu9dQwtwTExPDxo3/m5tmtVopUqQINWvWpH///lSoUCFH7797926+/fZbOnXqRIkSJXL0XiIi10vFk4iI5DmJqYkqnbxUVHAU995+L92rdqd5meakpqdespLJG0umrLpSGeVyuUh1prJ0z1Lm75rPdwe/IyU95ZLX1ylZhwD84KuvcjO2ZIdRo8Bi8ciZT+np6UycOJE333zTdBSjChUqxHPPPQdAWloaR44cYe7cuaxdu5avv/46Rwuh3bt388EHH9CgQQMVTyLicVQ8iYhInmJ32Bm4fKBKJy9SLqocHaI70KNaDyoXrowj3UFkUKT7wQCz2fKCv5dRPav3pGOljgRaA/npyE/M2jGLr//8mtOJpwF4uOrDhG659soo8VCvvQZly0KXLh5VPlWuXJmvvvqKJ598krJly+bYfRITEwkNDc2x979ZoaGhdOjQIdO1mjVr0q9fP9asWUNMTIyhZCIiZvmZDiAiIpJVNoeNiZsmMn3bdNNR5Cb4WfxoeGtD3mr9Fkf+dYTt/bczqsUoahWvRbB/8P9KJ7lufn5+5AvOR0hACK3LtWbCvRM4Ovgo25/czvBGw+lxRw/8J0w0HVNuRp8+sHUrJCebTpKhT58++Pv7M3Fi1v63tWDBAtq3b88dd9xB/fr1GTZsGCdPnsz0nH//+99UrlyZEydOMHDgQGrXrk337t0B99ylXr168dtvv/HQQw9RvXp1WrduzYoVKwDYvn073bt3p3r16jRv3pzFixdneu9z584xduxY2rdvT61atahRowYPPfQQ33//fTZ8NTIrVMg9xN9qtWa6npqayqRJk7jnnnuoWrUqjRo14sUXX+TcuXOZnrdmzRqefPJJmjZtStWqVWnatCkvvfQSFy5cyHjOhAkTGDFiBAA9evQgOjqa6Ohofvnll2z/fEREboRWPImISJ6QmJrImgNr+Pe3/zYdRW5AWEAYrcu15qEqD3Hv7fcCEOIfQoBVS5pyUkRQBAB3FL2DVwq+4t6m+O670LgxLFwIP/0E6emGU8p1SUuDdu3gt9+gVCn4R6FhQpEiRejWrRszZ86kf//+lC9f/orP/eSTTxg/fjx33nknw4YN49SpU3z++eds3ryZxYsXkz9//oznulwunnjiCaKjo3n22WdxOp0Zj/31118MHDiQTp060a5dO2bPns2QIUNwuVy8/vrrdO7cmbZt2zJr1ixGjBhB9erVM1ZjHT16lJUrV9KmTRtKlSpFYmIiy5Yto3///nz66ac0btz4hr4OTqeTs2fPAu7th0ePHmX8+PFERkbSsmXLTJ/X008/zfr16+ncuTPR0dEcPXqUmTNnsn37dubNm0dQUBAACxcuxGq18sgjj5A/f352797NggUL+OOPP5gzZw4ArVu3Ji4ujrlz5zJgwABuu+02AMqVK3dDn4eISHZT8SQiIh7Pke7gzzN/0m1BN1zoMNa85K7b7mJYw2G0KNOClLQUIoIi8LNowbUJQf7uH2QpXhz694dHH3WXFrNnu8uo3bvNBpSsu3ABmjd3r3yKijKdBoC+ffsyb948JkyYwHvvvXfZ55w9e5YJEyZQu3ZtPvvsM/z93T+K1KlTh379+vHJJ59kzEgCd5HTsGFDXnjhhUve6/Dhw0ybNo2GDRsC0LBhQ9q2bcuQIUOYNm0aDRo0AKBBgwa0bduWRYsWMXToUACio6NZvXp1plVIMTExPPDAA0yZMuWGi6djx45l3PeiokWLMnXqVIoWLZpx7euvv+b7779n6tSpNGrUKON6/fr1eeKJJ1i6dCldu3YFYPz48YSEZD6ts0aNGjz33HP8+uuv3HnnnVSsWJEaNWowd+5cGjVqRO3atW8ov4hITtHf/ERExKM5nU7OJJ6h1eetLjssWTxPZFAkT9d9miP/OsKXD33JPeXvIdg/mHzB+VQ6eQp/f8iXD8LD4bHHYPNm2LQJOnd2Pyae7/BhuOcesNtNJwGgcOHCdO/enZUrV17xFLuff/4Zh8NBz549M0ongLvuuovy5cuzdu3aS17z8MMPX/a9br311ozSCdyreyIiIrjlllsylT8Xrx89ejTjWmBgYEbp5HA4iI+Px2azUadOHXbu3Hldn/ffFSlShGnTpjFt2jQ+/fRTXn75ZUJCQnjyySc5ePBgxvOWL1/OrbfeSqVKlTh79mzGR+XKlYmIiGDDhg0Zz71YOrlcLmw2G2fPnqVWrVoAN5VVRCQ36W8WIiLi0eypdlrMaJExHFk8V7Wi1RjSYAhdK3cl3ZWeaei1eLCAAPdH7dowZQp88gl8+KH746+/TKeTq9m4EXr1gs8+Aw8Yut27d2+++OILPvjgAyZMmHDJ48ePHwe47ADycuXKXbZ4uvXWWy88qSl8AAAgAElEQVR7r+LFi19yLTIy8orX/z4TyeVyMW3aNObOncuhQ4cyPddisVz2flkRHBycqQwDaN68OXfffTdvv/12xtfk0KFDHD169JLVURedOXMm4/f79+/nrbfe4ueffyYpKSnT8/7+OYmIeDIVTyIi4rESUxPp8EUH9pzeYzqKXEGgNZBOlToxvNFwyhcoT6A1UHOb8rLI/w52f/ZZGDIE1q6FceMgB4YuSzZZsAAqVoThw90r2AwqWLAg3bt3Z8qUKezOhq2bVquVwMDAKz52Pdddrv9t0548eTLjx4+nQ4cOPPXUU0RFRWG1Wlm4cCFfffXVTef+u2LFilGmTBk2bdqUcc3pdFK2bFlefPHFy74m8r//HdpsNmJiYggKCmLQoEGULl2akJAQ0tPT6d27d6bPSUTEk6l4EhERj2R32Hnmm2f4/pB+4PVEpfKVYmCdgfSr3Q9AJ9F5m+Bg969t2rgHkZ8/7y6gPvvM/XvxLKNGQXQ0PPgghIUZjdK7d29mz57N+++/T4ECBTI9VrJkSQAOHDhwyQDyAwcOcMstt+RKxuXLl1O3bl3efPPNTNcXLlyYI/dLT08nMTEx48+lS5dm27Zt1K9fHz+/K28/3rBhA2fOnOHzzz+nbt26Gdf/vm3voptZqSUiktM0aEFERDyO3WHno80fMWXrFNNR5B/alGvDmkfXsOepPTxd72kigyJVOnkzPz+IiIBbboHXX4cTJ9zlU/XqppPJPz32GOzYASlmZ+FFRUXx6KOP8t1337Fr165MjzVs2JDAwEBmzJhBWlpaxvUffviBP//8k+bNm+dKRj8/v0tWCx06dIjVq1dn+70OHjzIwYMHqVixYsa1tm3bEh8fz2effXbJ89PT0zl37lxGTiDTaX4AU6Zc+r3x4iyohISEbMsuIpJdtOJJREQ8SlJqEmsPrWXY6mGmo8jftCzTkgn3TuCWyFuICIowHUdMuLiNq3t39xDyH36AwYPhCoOkJZelpcG998Jvv7mLwitsOcsNjz/+ODNnzrykeIqKiuLpp59m/Pjx9OrVizZt2nDq1Ck+//xzSpQoQZ8+fXIlX4sWLZgwYQLPPvss9erV4/jx48yZM4eyZcve1BbBxMREli5dCri39h07dowvvvgCp9PJ008/nfG89u3bs2rVKsaMGcPmzZupU6cO/v7+HDlyhFWrVjFo0CA6duxIrVq1iIqKYvjw4TzyyCOEhITw/fffc/bs2UvuXaVKFSwWCx9//DHnzp0jMDCQ+vXrU7BgwRv+fEREsouKJxER8Rip6ansj99P5/mdcaHZFZ7gzuJ3MuHeCdxR9A4NCxc3f3/3R+vWsHUrLFwI//43/HdwtBh07hw0bw5btkD+/MZi5MuXj549ezJx4sRLHuvbty9RUVHMmDGDsWPHEhYWRuvWrRk6dCj5cylzv379SE1NZenSpaxatYrbbruNF198kUOHDt1U8XT69Gmee+65jD9HRERQvXp1+vbtS7169TKuWywW3n//fT7//HMWL17Mf/7zHwICAihRogT33nsv9evXByB//vxMnjyZsWPH8uGHHxIYGEiTJk148803Lxlifuutt/LCCy8wdepUnn/+edLT05kxY4aKJxHxCBaXptKJiIgHcLqcxNnjqPZRNWLtsabj+LzbC9zO223epkWZFgRbg686h0R8XEoKpKe7T8N79VWIjzedSBo0gG+/9YiT7kRERFQ8iYiIR7A77NT9tC674nZd+8mSY4qHF2d0y9F0rdIVfz9/nVAnWZeU5C6gxoyBd96Bvw1TFgMefhgmTzY+bFxERETFk4iIGGd32OmzrA9zfp9jOorPyh+cn+ebPM+AOgOwWqwE+QeZjiR5ld0ODgeMHAmffuqePSRmTJ7snsmllU8iImKQiicRETEqMTWRBbsW0HNJT9NRfFKwfzD/qvcvRjYdib/Fn5CAENORxFskJLg/Bg+G+fNBf+XMfUFBsG0blC9vdNi4iIj4Ng1sEBERY5xOJycSTtDvq36mo/gcq8XKEzWf4NjgY7zQ9AUiAiOuWTpt376dUaNGcf/991OzZk0aN25Mv3792LFjxyXPtdlsvPrqqzRq1Ihq1arRtWtX1q1bl+V8LpeL6dOnc/fdd1O1alXatGnDjBkzLjkC/cCBA8TExFCzZk3uv/9+fvjhh8vmrlGjBkePHs3y/SUbRERAiRLuVU+7d0OrVqYT+Z6UFGjXzr0NUkRExBCteBIREWPsDjt3fnIne8/oOPbcVKlQJeZ1mcdt+W4jPCjrJ9UNGjSIzZs306ZNGypVqsT58+eZO3cux48f56OPPqJZs2aAuzSKiYlh+/btPPbYYxQvXpxFixaxc+dOpk2bRt26da95r3fffZdJkybRvn176taty4YNG/jqq6/417/+Rf/+/QFIT0/n/vvvx8/Pj+7du/Pjjz+yfv16VqxYQcmSJTOydOnShWbNmmU6zlwMsNlg1Sro0wcucxy85KBu3WDKFM17EhERI1Q8iYiIEXaHnQFfD2DG9hmmo/gMq8XKc42e44WmLxBkDcLqd31bb7Zs2ULVqlUJDAzMuBYfH0+7du0oWrQoixcvBmDlypUMGjSI0aNH07FjRwBSUlK47777iIiIYNGiRVe9z6lTp2jZsiX33XcfY8aMybg+dOhQVq9ezffff0+BAgXYv38/bdu2ZdasWdSuXZvk5GTq1avHyJEj6datGwDz589n0qRJrFixgqAgza0yLjnZvfrmscdg6VLTaXzL1KnuAkrznkREJJdpq52IiOS6pNQkvtz7pUqnXFSpUCV+e/I3RjYZSWhA6HWXTgC1atXKVDoBREVFUbduXfbt25dxbcWKFURGRtK+ffuMa0FBQXTu3JmdO3dy5MiRq95nzZo1pKam0qNHj0zXe/ToQXJyMmvWrAHcZRZAREQEAMHBwQQHB5P0321F58+f5+2332bkyJEqnTxFcDBERcGsWbBwIRQoYDqR7+jfH44fd588KCIikotUPImISK5yuSDIGkyjYq0pk7+M6Thez2qxMqLxCDb33UylQpUID8z61rqsio2NJX/+/Bl/3rVrF5UqVcLf3z/T86pVqwbAzp07r/p+u3btIiAggMqVK2e6XqVKFfz8/Ni1axcAZcqUITIyksmTJ3Ps2DGmT5/OuXPnMu7z3nvvUblyZVpptpDnCQuDtm1h3z7o0MF0Gt9wcd5TcrLpJCIi4mNUPImISK5KSoIGDSwsm1eQ7b33MbDOQNORvFZ2rHK6ls2bN7NlyxbatWuXcS0uLo7ChQtf8tyL12JjY6/6nnFxcURFRWH9xylcgYGB5M+fP+P1ISEhvPLKK3z77be0bNmSMWPG8Pjjj1OrVi327NnDwoULeeGFF272U5ScotVPue/PP92nDNpsppOIiIgP8b/2U0RERLKHzQavvw4bN8LGjRa+/trC7Nnv0yn6IdrMboHD6TAd0Svc7CynrIqNjWXIkCGUKFGCp556KuN6cnLyJVvygIztbhe3yF3JlV5/8T2S/7Zio23btjRu3JgDBw5QvHhxihYtCsBrr71GTEwMZcqUYcmSJUyZMgW73c4999zDkCFDLlmNJQb9ffWTZj/lvMmToVMnuOsu0BZUERHJBVrxJCIiuSI1FXbuhLFj/3dtxQqoVMlCwMmGHBsYT/1b6psL6CUqFqqY46ucABISEujbty+JiYlMmjQpY84SuGctORyXlogXC6drzVu60usvvkdwcHCma5GRkdSoUSOjdFq6dClHjx5lwIABbN68mREjRtCrVy/eeOMNli5dyieffHJdn6vkAq1+yl09eoDdbjqFiIj4CBVPIiKSK5KSoHNn94ynvzt5Epo0sfDOuBC+fWg9b7Z600zAPO7iLKdf+/6aY7OcLkpKSqJfv34cPHiQjz76iOjo6EyPFy5c+LLb6eLi4gAoUqTIVd+/cOHCnD17lvR/DEF2OBycO3fuqq+32WyMGzeO4cOHExoaypIlS6hduzadOnWifv36dOvWjSVLlmT1U5XcptlPuePMGXj4YUhMNJ1ERER8gIonERHJcXY79O4Nx45d/nGXC0aPttC8uYVHyg5lR58/KRCsFQ9ZVTSsKJv6bMrxVU7gLn8GDhzItm3beO+996hdu/Ylz6lcuTJ79uwhLS0t0/Vt27ZlPH41lStXJi0tLWOI+EW///47Tqfzqq+fOHEiZcqUyZg5dfLkyUxFVZEiRTh16tTVP0kx6++rn2bMgCtsu5SbtGqV++ur8klERHKYiicREclRSUnw9dcwf/61n7tpE0RHW/hjQzn29z9Jx4odcz5gHlereC129N9BlSJVcnSVE0B6ejpDhw5l/fr1jB07lrvuuuuyz2vTpg0XLlzgyy+/zLiWkpLCwoULqVy5MqVLl864fvbsWfbv309SUlLGtZYtWxIQEMCsWbMyve/s2bMJCgqiRYsWl73v/v37mT17Ni+++GLGtcKFC3PgwIFMz7nc4HPxQGFh0LEjbNgA11glJzdo8GA4dQqcTtNJRETEi1lffvnll02HEBER73X+PLRsCVcY2XMJhwPmzbNw8oQfk//VlRrFq7JgdxZaKx/UrUo3lnRbQmRwJP5+OT8se8yYMSxcuJCGDRtSoUIF9u7dm+mjVKlSBAQEUK5cOTZs2MC8efNITk7m8OHDjB49mn379jFu3DhuueWWjPf89NNPeeaZZ2jYsGHG9fDwcFJSUpg5cyZHjx7l3LlzTJs2jWXLlvHUU0/RrFmzy+Z79tlnadKkCQ8++GDGtaCgIKZMmcLp06f5888/mTZtGl27dqVBgwY5+8WS7BEYCAULwhNPwOrV7r25kn3S0uD776FXLwgIMJ1GRES8lMXl+ue0DRERkexhs8Ejj9z4IVW33w5Lv3QRVugMLWbXZ3/8/uwNmEdZsDC21VgG1BlAWGBYrt03JiaGjRs3XvHxNWvWZJRHCQkJjB8/nlWrVmGz2ahQoQLPPPMMTZo0yfSaCRMm8MEHHzBjxgzq1auXcd3pdDJ9+nTmzJnDiRMnKFGiBN27d6dnz55YLJZL7v3NN9/w2muv8c0332QadA4wY8YMpk+fnnGq3ciRI6854Fw8jNPpXj752GNZWz4p1+fdd6FPHwgNNZ1ERES8kIonERHJEamp8J//uFc73YzAQHjrLRePPe7i+R8H8/7G97MnYB4VERjBom6LqH9L/RzfWificex2mDABRo689KQCuXFhYXDoEBQqZDqJiIh4IRVPIiKSIxIToUoV988y2aFNG5gzx8WO+F9oPasZDmcW9+55kbJRZfk25luKhRcjJCDEdBwRM2w2WL8eOnVy/16yxwMPwOefQ7gKbRERyV4aLi4iItnObocxY7KvdAJYuRIqVbJgOV6PY0+fo8EtvjWjp0WZFmztt5VS+UqpdBLfFh4OTZrAtm1QpozpNN5jyRLYvNk990lERCQbqXgSEZFsd+aMu3jKbqdOQbNmFsaPCWb1Q+t4q/X47L+JB3qm3jMse3gZkUGRWP2spuOImBcSAqVKwdatcIXTFeUGPP541k+CEBERySJttRMRkWxlt8N998HatTl7n9q1YelSF/Ec4K5Z9TmdeDpnb2hAgF8AU9pP4cFKD2qek8iVJCbCc8/BxImmk3iH//s/99czLPcOLhAREe+m4klERLKNwwHLlkHnzrlzv/BwmDrVxd33pNP7m4dZsGtB7tw4FxQKLcTKR1YSXTA6V0+uE8mTbDZYuBB699ZWsZsVGAj79sGtt5pOIiIiXkLFk4iIZBubDcqVg9jY3L1vTIyLDz+EFYcW03Vhp9y9eQ4oFl6M9Y+vp0RECYL8g0zHEckb7Hb44Qf3kOzUVNNp8rbmzd3/iqBVTyIikg0040lERLKFzQb//nful04An39uoUYNC1X8HuTIU2e4vcDtuR8im9wSeQub+myiZGRJlU4i1yMszD3v6ZtvIEj/7dyU77+HVasgJcV0EhER8QIqnkRE5Ka5XHDkCEyaZC7D/v1Qo4aFRbOi2PL4HgbXH2wuzA26Lf9tbOqziaJhRQm0BpqOI5L3hIZC/frw7bfuAeRy4/r318oxERHJFtpqJyIiNy0xEZo2hV9/NZ3ErXVrmDvXxc74TbSe04zktGTTka6pfIHyrH98PVEhUfj7+ZuOI5K3JSXB779Dixbu5ZhyYwYOhNGj3QP1REREbpBWPImIyE1JSoLZsz2ndAJYvRoqVrTgPFaHo0+dpXGpxqYjXVWlQpX4pfcvFAgpoNJJJDuEhEDVqrBuHeTLZzpN3vXhh3DsmHtZq4iIyA1S8SQiIjclORmGDjWd4lKxsXDXXRbefCOYb7r+yDt3v2s60mVFF4xm3ePryB+UH6uf1XQcEe8REgIVKsBPP0FEhOk0eZPTCY884v4XBhERkRuk4klERG6YzQZPPw3nz5tOcnkuF4wbZ6FpUwudbx3Err4HKBRayHSsDGWjyrLu8XVEBkXi56dvySLZLjgYypeHtWvd85/k+v36K8yZo/JJRERumGY8iYjIDdu5072bJS8ID4fJn7q4t206fb/pwbxd84zmKZWvFJv6bNL2OpHckJQEv/3mnvmU7Pkz3zxOVBQcPeo+OVBEROQ66Z9XRUTkhthsMGSI6RRZZ7PBww9ZeKq/lSn3fsGCzouNZSkRUYKfn/iZAsEqnURyRUgIVK8OK1ZAoE6MvG7x8fDee+6TJERERK6TVjyJiMgN2bYNatQwneLGlC0LX37pIl/Rc7T6ogF7z+zNtXsXCSvCpj6bKB5enABrQK7dV0QAu9098+m++yAtzXSavCVfPvegcZ1wJyIi10krnkRE5LrZbDB4sOkUN+7AAahZ08K8Gfn59bFdPNvg2Vy5b2RQJOseX0exsGIqnURMCAuDxo1hntmttnnS+fMwfrxWPYmIyHXTiicREbkuLpd71mydOqaTZI9WrWDuXBe7z2+m1eymJKflzPwXP4sfq2NW0/CWhgQHBOfIPUQki2w2eOsteOUV00nylogIOH5cpwSKiMh10YonERG5LomJ8GzuLBDKFd9+CxUrWkg9XJtjT8XTtFTTHLnPuNbjqFuyrkonEU8QHg7DhkGHDqaT5C0JCTBmjHvLooiISBZpxZOIiGSZywW//AINGphOkv0sFhg82MWrr8HUHR8w6JtB2fbe3at255P7PyEsUCdCiXgUux3q14fffzedJO8IC3OvesqXz3QSERHJI1Q8iYhIltnt0Lo1/Pyz6SQ5p2ZNWLrUhd3/CM1m1iU2Mfam3q92idqs7blWpZOIJ3I6ITYWqlaFM2dMp8k7hg2Dl15yl1AiIiLXoK12IiKSJU4nbNni3aUTwNatUKmSha1rS/Hnk8d5uOrDN/xexcKL8U2Pb1Q6iXgqPz+IioLly8Hf33SavOODDyA11XQKERHJI1Q8iYhIliQleddsp6ux26F7dwtP9rPySZtZLO6yDAuW63qPIGsQqx5ZRUSQhvCKeLSgIKhSBSZNMp0k70hKcg9mt9lMJxERkTxAxZOIiFyT0+me7bRpk+kkuWvOHAvVqlkol96OowPPUqlQpSy/dsaDMygXVY5Aa2AOJhSRbBEWBg8/DP37m06Sd0yaBA6H6RQiIpIHqHgSEZFrSk72ndVO/3TwINSqZWHOtHxs7PU7zzV67pqvGdZwGG1vb0toYGguJBSRbBEWBm+9BU1z5mRLr5OSAi++qFVPIiJyTRouLiIiV5WeDt9+C/fcYzqJeS1awLx5Lv5I2ELLWU1ISku65DltyrVhUbdFhAaodBLJk86dgxo14PBh00k8X2Cg+4S7QoVMJxEREQ+mFU8iInJVKSnuA4wEvvsOKla0kHSgFkefOstdt92V6fEKBSswv8t8lU4ieVl4uLtt14lt1+ZwwAsvaNWTiIhclYonERG5ovR0d9myY4fpJJ7j9Glo2dLCqFeC+KrLd3xw70QA8gXl49uYb1U6ieR1/v5QsiTMnw+W6ztUwCdNmeI+kUFEROQKtNVORESuyG6Hhg1h+3bTSTxT9erw5ZcukgOPcSRxL41LNSbYP9h0LBHJDjYbvPwyjB9vOonne/JJGDfOvVpMRETkH1Q8iYjIFf32G9SsaTqFZwsNhRUrXDRpAhatjhDxLomJ7oZ53z7TSTxbWBicOqXtiSIiclnaaiciIpeVkABjx5pO4fnCwqB6dYtKJxFvFBQE8+aBn/7KfFV2O8yZA6mpppOIiIgH0ndRERG5LKcTFi0yncLzTZsGwdpdJ+KdrFa4/XYYPNh0Es/3zjsqnkRE5LJUPImIyCVSUuDjj90HFsmVdekCzZq5F0WIiJcKD4dXX4Xy5U0n8Wy7dsHu3aZTiIiIB9KMJxERuURSElSsCEeOmE7iuQoXhj//hHz5TCcRkRyXnu4+ZaF2bfdyULm8Ll3g008hMtJ0EhER8SBa8SQiIpdYv16l07Voi53Ijdm+fTujRo3i/vvvp2bNmjRu3Jh+/fqxY8eOS55rs9l49dVXadSoEdWqVaNr166sW7cuy/dyuVxMnz6du+++m6pVq9KmTRtmzJjBP//d9cCBA8TExFCzZk3uv/9+fvjhh8xvZLWy3eGgRnQ0R/39b+jz9glLlqiYExGRS6h4EhGRTBIS4M03TafwbNpiJ3LjPv30U5YvX07t2rUZMWIEPXv2ZP/+/XTt2jVT4eNyuXjyySdZsGABnTt3ZuTIkQD07duXjRs3Zule7733HqNHj6Z69eq89NJLVK1alddff52PPvoo4znp6ekMHDiQ+Ph4hg0bRsmSJXn66ac5fvx4piyvjhvHE717c+ttt2XPF8IbpabCpEmQnGw6iYiIeBBttRMRkUxOnICSJUHfHS5PW+xEbs6WLVuoWrUqgYGBGdfi4+Np164dRYsWZfHixQCsXLmSQYMGMXr0aDp27AhASkoK9913HxERESy6xukHp06domXLltx3332MGTMm4/rQoUNZvXo133//PQUKFGD//v20bduWWbNmUbt2bZKTk6lXrx4jR46kW7duAMyfP59JkyaxYtkygv74Q1vurqZkSdi3T0tCRUQkg1Y8iYhIhsRE98FEKp2uTFvsRG5OrVq1MpVOAFFRUdStW5d9+/ZlXFuxYgWRkZG0b98+41pQUBCdO3dm586dHLnGfuA1a9aQmppKjx49Ml3v0aMHycnJrFmzBnCXWQAREREABAcHExwcTFJSEgDnz5/n7bffZuTIkQSFhemUu2s5fhx+/FHfSEREJIOKJxERyWCxwJQpplN4Lm2xE8k5sbGx5M+fP+PPu3btolKlSvj/Y6ZStWrVANi5c+dV32/Xrl0EBARQuXLlTNerVKmCn58fu3btAqBMmTJERkYyefJkjh07xvTp0zl37lzGfd577z0qV65Mq1at3G+gU+6u7c03wWYznUJERDyEpiOKiAjgPrTpyy/h7FnTSTxT4cIwebL7Z04RyV6bN29my5Yt9OrVK+NaXFwcd9xxxyXPLVy4MOAuqq4mLi6OqKgorFZrpuuBgYHkz58/4/UhISG88sorjBw5kmXLlmGxWHj88cepVasWe/bsYeHChSxZsoR/vAnMm6ctd1fy3Xdw4QL8dxWZiIj4NhVPIiICQFISjB9vOoXn+vRTbbETyQmxsbEMGTKEEiVK8NRTT2VcT05OvmRLHri328H/tshdyZVef/E9kv82ALtt27Y0btyYAwcOULx4cYoWLQrAa6+9RkxMDGXKlGHJkiVMmTIFu93OPffcw5C+ffEfNAjeffe6P2ev53K5v6G89hqEhZlOIyIihmmrnYiIAO6xHJs2mU7hmerVg5YttcVOJLslJCTQt29fEhMTmTRpUsacJXDPWnI4HJe85mLhFHSN/yCv9PqL7xH8jyY5MjKSGjVqZJROS5cu5ejRowwYMIDNmzczYsQIevXqxRtvvMHSpUv5ZPZs95a7yMjr+px9xrRp4KcfNURERMWTiIgACQkwdqzpFJ5rwgQICTGdQsS7JCUl0a9fPw4ePMhHH31EdHR0pscLFy582e10cXFxABQpUuSq71+4cGHOnj1Lenp6pusOh4Nz585d9fU2m41x48YxfPhwQkNDWbJkCbVr16ZTp07Ur1+fbt26ubffWa0wcmRWP2Xfcu4cLF7s3sctIiI+TcWTiIhgscAXX5hO4ZnatIFKlfQP9yLZyeFwMHDgQLZt28Z7771H7dq1L3lO5cqV2bNnD2lpaZmub9u2LePxq6lcuTJpaWkZQ8Qv+v3333E6nVd9/cSJEylTpgzt2rUD4OTJk5mKqiJFinDq1CkIDYWnn4Zixa7+Cfuq8ePhb1saRUTEN+mv0SIiPs7phCVL3DOeJDOLxb3aSQPFRbJPeno6Q4cOZf369YwdO5a77rrrss9r06YNFy5c4Msvv8y4lpKSwsKFC6lcuTKlS5fOuH727Fn2799P0t/+H1nLli0JCAhg1qxZmd539uzZBAUF0aJFi8ved//+/cyePZsXX3wx41rhwoU5cOBApudcHHKOnx+MGpXlz9+nbNmiEytERETDxUVEfF1CAsycaTqFZ+rWTQsZRLLb2LFjWblyJY0aNSI9PZ2lS5dmerx169aEhobSpk0b7rzzTl5++WUOHz5M8eLFWbx4McePH2fq1KmZXjNr1iw++OADZsyYQb169QAoWrQoTzzxBB999BFOp5M6derwyy+/sGzZMgYNGkTBggUvm2/UqFF07dqVChUqZFxr27YtixYt4qWXXqJkyZLMnTuXxx57zP1gcDA8/DCMHg3792fjV8pLzJwJzz7rPglQRER8koonEREf5+/vPvlaMgsIgHfe0WngItlt9+7dAKxbt45169Zd8viaNWsIDQ3Fz8+Pjz/+mPHjxzN//nxsNhsVKlTg448/pn79+lm61zPPPEO+fPmYM2cOy5cvp0SJEowYMYKePXte9vnffPMNf/zxB++//36m602aNOH5559n+vTp2DaqDDgAACAASURBVO12HnzwQQYMGPC/JwQEuLeVPfBAFr8KPmTuXPd2RBVPIiI+y+JyuVymQ4iIiBkuFyxcCF26mE7ieQYMcA9c1zY7EcmSxERo1Ah++810Es9z4oSWj4qI+DDNeBIR8WEXLsDnn5tO4XnCwuD111U6ich1CAqCf6yUkv+aPRtSU02nEBERQ1Q8iYj4sMBAWLXKdArP8+yz7p0zIiJZZrVCzZpwhWHpPu2LL3S6nYiID9NWOxERH7ZsGbRvbzqFZylYEA4d0monEblBO3dC1aqmU3ieuDgoVMh0ChERMUArnkREfNSFCzBjhukUnuell9wLF0REbkjp0vDgg6ZTeJ758yEtzXQKERExQCueRER8VHKy+x+f7XbTSTzHrbfCnj0QGmo6iYjkaUeOQNmykJ5uOonnaNQIli+HyEjTSUREJJdpxZOIiI/66SeVTv80Zgz4+5tOISJ5XoEC8OijplN4lvXrwek0nUJERAxQ8SQi4oMuXIDp002n8CyFC7t3xwQGmk4iInleeDj83/+ZTuFZXC5YtEirwEREfJCKJxERHxQUBF99ZTqFZ+nd2/1zkYhItihUCJo0MZ3Cs8yaBTab6RQiIpLLNONJRMQH/fgjNGtmOoXn8PODkyfdq55ERLKF0+lu+Dt0MJ3Ec1itEB8PERGmk4iISC7SiicRER9js2mb3T+1beteBSYikm38/KB1ayha1HQSz5GeDl9+qVlPIiI+RsWTiIiPCQiApUtNp/Aszz2ng5ZEJIf062c6gWeZORMSEkynEBGRXKStdiIiPmbjRqhXz3QKz1G2LPz+O4SEmE4iIl7pzBn3qicN1XYLCHBvtwsLM51ERERyiVY8iYj4kORkmDfPdArPMmiQe0eMiEiOCAiA++4zncJzpKbCTz+ZTiEiIrlIf9UWEfEhDgf88IPpFJ4jOBgef1zznUQkB0VGwvDhplN4luXLISnJdAoREcklKp5ERHxIYCBs3Wo6hefo2tV0AhHxCdWrw+23m07hOX780b3ySUREfIKKJxERH/Lbbxoz8nfDh+tUbxHJBf7+7n294rZ9u3sLooiI+AQVTyIiPsLhcO9uELc774TSpU2nEBGfEBgIvXrpFIOLnE749VfTKUREJJeoeBIR8RFJSbB2rekUnmPIEM12EpFc5HLBQw+ZTuE5vv4aUlJMpxARkVxgcblcLtMhREQk56WkQL58+ns+QP788NdfWnwgIrnsjz8gOtp0Cs/QsKF7GW6+fKaTiIhIDtOKJxERH7F3r0qni2Ji3Ds9RERyVYkSUKuW6RSeYfNm99GiIiLi9VQ8iYj4gPR0+OYb0yk8x6OPQliY6RQi4nMCA6FLF9MpPIPDAbt2mU4hIiK5QMWTiIgPsNngu+9Mp/AM+fPDHXeYTiEiPikwELp1M53CcyxfDmlpplOIiEgOU/EkIuIDQkJg/XrTKTzDvfdqy6GIGFSsGNx2m+kUnmHtWrDbTacQEZEcpuJJRMQHHD4MCQmmU3iGhx+GyEjTKUTEZzmdcP/9plN4hp9/1ikPIiI+QMWTiIiXczph9WrTKTxDQAC0bGk6hYj4tLAweOQR0yk8g90OBw6YTiEiIjlMxZOIiJdLSIBvvzWdwjM0bQqpqaZTiIjPq15dSy8vWrXKfQKGiIh4LRVPIiJeLjgYfvrJdArP0LkzhIebTiEiPi8lBe65x3QKz7BmjfsEDBER8VoqnkREvFxcnPtDoGNHsFpNpxARnxcZ6R44J+5/GQkONp1CRERykIonEREv98MPphN4hqpVITTUdAoRkf9q3Rr8/U2nMO/sWTh1ynQKERHJQSqeRES8WHIybNxoOoVneOAB/YwnIh4kLQ0aNzadwjP8/rvpBCIikoNUPImIeLHkZNizx3QKz9C9u3ZziIgHCQtzD54T+PVXDRgXEfFiKp5ERLxYYCDs3m06hXlFi0KZMqZTiIj8jb8/dOpkOoVn2LkT7HbTKUREJIeoeBIR8WJWKxw7ZjqFeffd597VIiLiUSIioFIl0ynM270bXC7TKUREJIeoeBIR8WJHj+rv8uDeZhcebjqFiMg/WK3QoYPpFOb98YdOfxAR8WIqnkREvJjmtbrVrWs6gYjIZQQHQ5s2plOYl5wM8fGmU4iISA5R8SQi4qVSU2HzZtMpzCtdGiwW0ylERK6gRg3TCTzDH3+YTiAiIjlExZOIiJdKTNRgcYA779R8JxHxYMHBUKyY6RTmbdmiveEiIl5KxZOIiJfy81PxBFCvnuY7iYgHS052N+S+bvt2nWwnIuKlVDyJiHip4GDYt890CvOaNnXP7xUR8UhhYVCnjukU5u3ereWpIiJeSsWTiIiXio11z3nydVWrmk4gInIVAQHQrJnpFObt2QMhIaZTiIhIDlDxJCLipfbuNZ3APA0WF5E8oXp10wnMO3sWUlJMpxARkRyg4klExAs5nfDrr6ZTmKfB4iKSJ4SEaMA4wIEDphOIiEgOUPEkIuKF7HbYscN0CvM0WFxE8gQNGHfbts10AhERyQEqnkREvJDT6R6X4es0WFxE8gQNGHfbutVdwomIiFdR8SQi4oVCQlQ8gQaLi0geoQHjbrt3q3gSEfFCKp5ERLxQUhIkJJhOYZYGi4tInqIB4/Dnn+DvbzqFiIhkMxVPIiJeKD7edALzNFhcRPKUkBAoWtR0CrNiYyEoyHQKERHJZiqeRES80JkzphOYV7euBouLSB6iAePukzFERMTrqHgSEfFCsbGmE5jXsKEGi4tIHhIWBjVqmE5hnq/vExcR8UIqnkREvNBff5lOYN4tt5hOICJyHQICoFw50ynMO3fOdAIREclmKp5ERLyMywXHj5tOYV6hQqYTiIhcp9tuM53APO0VFxHxOiqeRES8THIyxMWZTmFWQACEhppOISJynUqUMJ3AvFOnTCcQEZFspuJJRMTLpKbC6dOmU5hVtKi7gBMRyVOKFDGdwDztFRcR8ToqnkREvIzTqRVPJUq4CzgRkTwlMhIsFtMpzDp+3P2NTEREvIaKJxERL2OxaMVT8eL62U1E8qC0NChY0HQKs+LitGRVRMTLqHgSEfEy/v4qnooXd895EhHJU1JSNOfp9Gl3ASciIl5DxZOIiJcJDlbxVLKk++sgIpKnuFzu5tyXnT6trXYiIl5GxZOIiJdJTXX/o7kvK1sW/PQdTkTyGn9/rXiKi9NeaRERL6O/louIeJmEBNMJzCtd2nQCEZEbEBysFU+nT2uvtIiIl1HxJCLiZeLjTScwz9d/bhORPMrf371k05edOQNBQaZTiIhINlLxJCLiZXx9vhPoUCgRycN8fclmaio4HKZTiIhINlLxJCLiZU6eNJ3ALKsVwsNNpxARuUElS5pOYN6FC6YTiIhINlLxJJJFMTExxMTE5Og9oqOjmTBhQo7eQ7IuN/5vnhPi4kwnMKtoUQ1XF5E8rHBh0wnMO3/edAIREclGPl88LVq0iOjo6IyPSpUq0ahRIwYPHszBgwdNx7thv/zyCxMmTMD2/+zdeXhTZd7G8W/SvUDZLAhVkNFppZayWDYpIgWEASmiyCJWRUcRVFDAl03FYXAAFWZEGeBlkRcHHGQHZUcFBQURRXaGouwUsC3dlyTn/aM2QyiUAm1Pmt6f6/Ly6pOTc+6TpCn55Xl+Jy3N7CgukpKSiIiIYMCAAYVu1717d+677z5sNluxZzhw4AAffPABp0+fLvZ9l0Vnz57l7bffplOnTjRs2JBGjRoRGxvLpEmTOHfuXIkee+PGjXz44Ycleoxr8cTXQ1aW2QlKn5/fzwQHj6Nu3a5UrNiYDh2i6d+/P3v27CmwbVpaGmPHjqVVq1ZERkbSs2dPtm7dWuRjGYbB3LlzefDBB4mIiKBjx47MmzcPwzBctjt69ChxcXE0btyYrl27snnz5gL7+vnnn2nUqBEnTpy4/pMWEc9UubLZCcyXm2t2AhERKUblvvCUb+DAgbzzzjuMHTuWDh06sGHDBnr37l3iH7xLyo4dO/jwww/drvBUtWpVWrduzddff01ycvIVtzly5Aj79++nS5cueHt7F3uGAwcO8OGHH153oWH27NnMnj272POYadu2bXTu3JnFixfTvHlzRo4cyfDhw2nUqBELFy4s8dk+Gzdu5J///GeJHuNaCns9lNXnvATqtW6vWrVZVKq0mszMKAICRtK791PEx8fTs2dPl4KPYRi88MILLF68mB49ejBq1CgAnn/+eXbs2FGkY73//vuMHz+ehg0bMmbMGCIiInj77beZPn26cxu73c5LL71EUlISr732GiEhIbz88sucOnXKJcvYsWN59tlnuf3224vpkRCRMs+qf57jcJidQEREilHxf6ovo1q1akVUVBQAjz32GPXq1eNvf/sby5Yto3///le8T0ZGBoGBgaUZ03S5ubkYhoGvr+8N76Nbt2588cUXrF69mscff7zA7StWrHBu5w4yMzMJCAi4qXM2g2EYZGVlERAQcMXbT58+zaBBg6hevTr/93//R+3atV1uHzZsGDNnziyNqEXicDjIzc3FrxSvdFPWnvN85bEna1LS05w58x7gy113QVwcPPFED7p06cI//vEP2rRpA8D69ev5/vvvGT9+PI888giQN8PyoYceYsKECSxdurTQ4yQkJDBr1iy6d+/OhAkTgLy/GRaLhenTp9OrVy+qVavGr7/+Snx8PPPnzycqKopHHnmE5s2b880339CrVy8AFi9eTGJiIs8//3zJPTAiUvZ4eZmdwHzl8RsUEREPpq9UruK+++4D4OTJkwB88MEHhIWFcejQIUaOHEnz5s2dH2QAvv32W5588kkaN25Mo0aNeOKJJ9i5c6fLPvP3ceTIEV5//XWaN29OVFQUb775Jjk5OaSnp/Pmm2/SokULGjduzPDhw8m6bM3M0qVL6devH9HR0URERNCuXTsmTZpEziWfNEeMGOFcvtSmTRvnMsL8c7laH6HL+9ls376dsLAwli9fzrRp04iJiSEyMpL4+HgAjh8/ztChQ2nRogURERE89NBDfPrpp9d8bGNiYggKCmLlypUFbjMMg88++4w777yTiIiIIh/n0qxz5syhbdu2NGjQgJ49e7Jv3z6X52DkyJEA9O3b1/nYbN++3Znt6aef5vvvv6d3795ERkYyefLkKz4++Xn//e9/88gjj9CwYUOaNm1Knz592Lhxo8v5jhgxosC5jhgxgpiYmEIfq+TkZCZOnEhsbCxNmjShUaNG9O7dmy+//LLAtmFhYYwePZr169fTrVs3GjRowMKFC6+671mzZpGamsrbb79doOgEEBQUxNChQ13GvvjiC3r27EnDhg2JiopiwIABztdDvvzXeXx8PG+++SbNmjWjcePGDBo0iKSkJOd2cXFxLFu2DLvd7rLcFfJ+78LCwvjnP//Jv//9bzp16kSDBg34+uuvAZgzZw6PP/648zXRqVMnZs+eXWCpE8D+/ft58cUXad68OQ0aNKBjx468/fbbzqyFvR6u9JxnZ2czefJkYmJiiIiIICYmhsmTJ7v8DsJ/X0s///yz87XUpk0bPvroo6s+J8WlPP57PSurCZBXKPTxyRurWrUqzZo148iRI87t1qxZQ1BQELGxsc4xPz8/evTowb59+zh+/Hihx9m0aRO5ubn07dvXZbxv375kZWWxadMmIO91AlCpUiUA/P398ff3JzMzE4CLFy8yefJkRo0aVarFVBEpAxwOKIEZ32WK3W52AhERKUbl/K/a1R07dgzI++ByqaFDh1KzZk0GDRpEeno6AKtXr2bo0KE0bdqUwYMHA3kFoqeffpqPPvqIpk2buuwjf9nF4MGD+eGHH1i4cCEBAQH85z//ITAwkEGDBrFr1y6WL19OzZo1GTJkiPO+8+fPp169ekRHRxMYGMiPP/7IrFmzOH36NJMmTQKgV69epKWlsWHDBl5//XWCgoIAqFat2g09FrNmzcLhcPD444/j7e1N5cqV+eWXX+jduzeVK1fm6aefJigoiC1btvDGG2+QnJxc6Df4vr6+dOrUiU8//ZQTJ064LDH5/vvvOX36tPOcr/c48+bNIzc3l7i4OGw2G7Nnz+all15i/fr1+Pj40KFDB86fP8/ChQsZOHAgd9xxBwB33nmncx8nT55k4MCBPProozzyyCOFPm5jxoxh4cKFNGvWjMGDB+Pt7c2ePXv45ptvaN++/Y083C5OnDjBunXr6NixI3Xq1CEjI4NVq1YxYMAAZs2aRXR0tMv2P/74Ixs3bqRPnz48/vjjzvO7kk2bNhESEkKzZs2KlOWzzz5j2LBhhIaG8sorr5CWlsa//vUvevfuzeLFi6l72eWfX3vtNW655RYGDx7MsWPH+Ne//oW3t7ezkPfCCy/gcDjYtWuXc+bI5VavXk1qaiq9evWicuXKhPx+pZ+PPvqIBx54gI4dO+Lt7c3WrVt55513SElJ4dVXX3Xef/v27Tz33HNUqlSJ3r17U7t2bU6cOMGaNWsYPXp0kV4PlzIMg5deeoktW7YQGxtLo0aN+PHHH5kxYwaHDx92WWoFcObMGQYMGEBsbCyxsbGsWbOGCRMmcNddd9G6desiPe7XyzDKZ+HpUpd+Xjt37hxVqlRx/rx//37q169fYBlvZGQkAPv27aNOnTpX3ff+/fvx8fEhPDzcZfyee+7BarWyf/9+AOrVq0dQUBAzZ87klVdeYePGjSQnJzuP8/777xMeHl4s7xMi4mHyC0/l+c28PJ+7iIgnMsq5JUuWGKGhocaXX35p/Pbbb8bZs2eNTZs2GW3btjXuvvtuY+/evYZhGMaUKVOM0NBQ44UXXjAcDofz/unp6UazZs2MIUOGuOw3MzPTaN++vdGrVy/nWP4+hg4d6rJtjx49jLCwMOO1114rMH7fffe5jGVkZBQ4h6lTpxphYWHGmTNnChzr0rF8oaGhxpQpUwqMP/HEE8YTTzzh/Pm7774zQkNDjejoaCM1NdVl22eeecZ48MEHjfT0dJfxIUOGGA0bNjRSUlIK7P9S33//vREaGmpMnTrVZXz06NFGWFiYcerUqes6Tn7WmJgYIzMz07ndhg0bnM9vvvzn/Pvvvy+Qq23btkZoaKixbt26Ardd/vjs2LHDCA0NNUaMGOHymjAMw+Xntm3bGsOHDy+wv+HDhxtt27Z1Gbv8ucnOzjZsNpvLNtnZ2caf/vQn4+mnny5w37CwMGPPnj0FjnW51NRU5+u5KHJycoxWrVoZHTp0MNLS0pzjBw4cMO6++25j0KBBzrH8194rr7ziso9x48YZ9evXd3ltDB8+3Khfv36B4504ccIIDQ01IiMjr/gavtLvwahRo4xGjRoZ2dnZhmEYht1uNzp06GC0bNnSOH/+vMu2lz4/hb0eLn/Ov/jiCyM0NNR47733XLabMGGCERoaanz11VfOsfzX0ubNm51j2dnZRsuWLY2XX365wLGKi81mGKNGGUZeCap8/texo2EkJeW9z4SFhRnjx493Pj6NGjUq8H5tGIbxn//8xwgNDTXmzp1b6OP7/PPPG9HR0Ve8rUWLFsbAgQOdP3/++edGw4YNnb+bEyZMMAwj7/cmMjLSOHr06I08xSLi6bKzDaNiRfPfTM3875tvzH4WRESkGGmp3e/69+9Py5Ytuf/++xkwYADZ2dm888473HPPPS7b9enTB4vF4vx527ZtJCcnExsbS2JiovO/jIwMWrZsye7du51LK/L17t3b5edGjRphGAY9e/YsMH7hwgWX++f363E4HKSkpJCYmEjTpk0xDMNlSVlx6tatGxUrVnT+fPHiRbZu3UqnTp3IyspyOe/WrVuTmZnJTz/9VOg+7733XkJCQli1apVzLCcnh3Xr1tG0aVNq1659Q8fp3r07/v7+zp/zZ5tdzxWjgoOD6dChwzW3W7t2LQCvvPKKy2sCKPDzjfL19cXr914POTk5JCUlkZaWRtOmTa/4fEdGRjqXKBYmv+n8pc9rYfbt28f58+fp06cPFSpUcI7ffffdtG7dmi1btuC4rBHo5f27mjVrht1uv66m7jExMdx6660FxvN/D2w2GxcvXiQxMZFmzZqRkZHB0aNHgbyZKceOHePJJ5/klltucbn/jT4/+Uscn3nmGZfx5557zuX2fLfddhv333+/82dfX18aNmzoXPZaEgxDKxS8veHChXMMGTKE2rVr8+KLLzpvy8rKumLvrvzlbvlL5K7mavfP38ely6M7d+7Mli1bWLhwIZs3b2b48OEA/PWvfyUuLo569eqxfPlyunbtSkxMDO+8806JXMlTRMoYLbXTjCcREQ9Tzv+q/dfIkSMJDQ3FarVSrVo17rzzTucH/ktdvgTjl19+ASh0aVlycrJLg+datWq53J6/FO5q4ykpKc77//TTT0yePJkff/yxQE+Z1NTUQs/xRl1+zseOHcMwDKZPn15gaVG+3377rdB9WiwWYmNjmTZtGnv27KFBgwZ88cUXpKSkOJuK38hx8pdi5av8+yWJL168WGieS912221FKkwcO3aMKlWqULNmzSLv+3oZhsFHH33EwoUL+fXXX11uu1LGol4ZK7/glL9c9Fryr8T1hz/8ocBtd955J5s3byYxMdGlwHN536j81/P1PBdXW/K0efNmpk6dyr59+wp8UM//Pcjv1RMaGlrk413LqVOnqFatWoEluPljlxeUrtQ7q3Llyhw6dKjYMl1OhSewWlN59dXnycjIYP78+c4+S5DXa+ny9074b8HpWv2Wrnb//H1cWviGvNd9o0aNnD+vWLGCEydOMHPmTHbu3MnIkSMZN24cISEhDB06lIoVKzJw4MAin6uIeCAfH/jsM8jNNTuJeX5fliwiIp5BhaffRUREOK9qV5jLP1QYhgFw1QbNULC30pUKWoWN5x/jxIkTPPXUU9SpU4cRI0ZQu3Zt/Pz8SEhIYMSIEQVmnFwvu91+xQyXfxDLP05cXNxVm2Pfdddd1zxet27dmDZtGitXrqRBgwasXLkSPz8/OnXqdMPHsV7lEsT5j2FRXP4clxR7EaoDM2fOZNKkSXTr1o0XX3yRqlWr4uXlxZIlS/jss88KbF/U7BUrVqRmzZolWgC51uu5KK5UBNi1axcvvPACjRo1YsyYMdSsWRMfHx/27dvHe++9d9O/B8Xpao+BlByLJZPjx/vjcPzCnDmznQ3r8wUHB3Pu3LkC9zt//jwANWrUKHT/wcHBJCYmFni/zMnJITk5udD7p6Wl8e677zJy5EgCAwNZvnw5UVFRPProo0Bef77ly5er8CRS3lkcUHUb2Iv25ZBH8qkLVL3mZiIiUjao8HST8mdkVK1a1XklvJLyxRdfkJWVxfTp011m9nzzzTcFti1sxk7lypVJSUkpMH7q1KlCm+rmy9/Gy8vrps65Xr16REZGsnr1agYMGMCWLVvo0KGDczZOcR3ncsW1DK5u3bp8/fXXJCQkFDrrqbDH+1pWr15Ns2bNeOedd1zGlyxZcv2BLxMTE8Mnn3zCzp07r1l0zX+9HT161OVqjvljgYGBN9S8/kaei7Vr1+Lj48PcuXNdClOXL6fMf/0cPny40KsHXk+GkJAQvvnmG5KSklxmPSUmJpKUlMRtt91W5H2VFIulPF+JO4fatV8iM3M3kyZNveLrOjw8nK1bt2Kz2VwajO/evdt5e2HCw8NZtGgR+/fvp0GDBs7xvXv34nA4Cr3/1KlTqVevHl26dAHg7NmzLoWqGjVqkJCQULRTFRHPZdhh398gN9nsJOap0RYq1jM7hYiIFBP1eLpJ0dHRBAUFMW3atCsuv0hMTCy2Y+XP5rl0RofD4bji5dnzl+ZdqeBRp04dduzY4TK2ceNGzp49W6Qc1apVo2XLlixevPiKxZPrOedu3bpx4cIF3nzzTXJzc53L7Ir7OJfKf2xudmli/sysf/zjHwVm8Vz6c506dfjpp59cXh/79u3jxx9/vOYxrFZrgX3/+uuvbNiw4WaiA/DnP/+ZChUqMHr06Cs+96mpqc4rJUZERBAcHMy///1vMjIynNscPnyYr7/+mjZt2lx1tllhAgICsNvtRV7yB3mPicVicZkxlp2dzb/+9S+X7cLDw6lTpw7z5s3jwoULLrdd+phez+shv4A1d+5cl/HZs2cD0LZt2yKfR0kpv4UnO7VqDSMwcBvBwROJjn7gilt17NiRlJQUVq5c6RzLzs5myZIlhIeHu1ydMTExkfj4eJc+e+3atcPHx4f58+e77HfBggX4+fldtcgZHx/PggULeOONN5xjwcHBzp5k+dsEBwdf11mLiCeyglHOexxZ9d24iIgn0bv6TapYsSJjx45l6NChdO3ala5duzq/tc4v7nz88cfFcqzWrVvj6+tL//796dWrF4ZhsGbNmisWvPKbok+ePJlOnTrh4+ND27ZtCQwMpFevXrz++usMGDCANm3acPToUT7//PMizXbKN2bMGPr06UO3bt147LHHuOOOO7h48SIHDhxg48aN7Nmzp0j76dy5MxMmTGDDhg1Uq1aN6OjoEjnOpe655x4sFgszZswgOTkZX19fWrRoQfXq1a9rP02bNuWxxx5j0aJFnDx5krZt2+Lr68u+ffvw9/dnzJgxQF4z+bVr1/LMM8/QuXNnEhIS+PTTT/njH//obPJ9NTExMXzwwQcMHTqU5s2bc+rUKT755BP+8Ic/cODAges+90vddtttvP/++wwaNIjOnTsTGxtL/fr1MQyDQ4cOsXr1aqpUqcLQoUPx9vZmxIgRDBs2jD59+tC9e3fS0tL4+OOPqVChAq+++uoNZch/nY4bN46WLVvi5eXlnA1yNTExMcydO5d+/foRGxtLeno6y5cvL7Asz2q1MnbsWJ577jm6detGjx49CAkJ4dSpU6xevdpZvLue10ObNm1o3bo106dP5+zZs0RGRrJ7925WrFhB27ZtC8wGM4PVWj570gYHT6RSpXWkp7fCbrezZs0KAgP//8/JHwAAIABJREFUe3uHDh0IDAykY8eO3Hvvvbz11lscO3aMWrVqsWzZMk6dOsWcOXNc9jl//nw+/PBD5s2bR/PmzQGoWbMmzz77LNOnT8fhcNC0aVO2b9/OqlWrGDRo0FXfR8aNG0fPnj1deo517tyZpUuXMmbMGEJCQli4cCH9+vUr/gdHRMoWiwpPWMrlNygiIh6rHH48KX5/+tOfqFmzJjNmzGDevHlkZmYSHBxMgwYNnL07isMdd9zBtGnTmDx5MpMnT6ZixYp07NiR3r1707VrV5dtW7RowQsvvMCSJUvYvHkzDoeDTZs2ERgYyKOPPsqpU6dYtGgRW7duJTIyklmzZvG3v/2tyFnq1avH0qVLmTp1Kp9//jmJiYlUqVKFO++8kxEjRhR5P/nFpi+//JIuXbq4LH0pzuNc6vbbb+f1119nzpw5jB49Grvdzrx586678AR5V6eqX78+Cxcu5O9//zsBAQHcdddd/PnPf3Zu07JlS+fxxo8fzx//+EcmT57MihUrCsw8u1z//v3Jzc1lxYoVrF+/njvuuIM33niDX3/99aYLT5BXzPz888+ZM2cOX3/9NUuXLsVisVC3bl169+5NXFycc9uHHnqIgIAApk+fzuTJk/Hx8aFZs2YMHTrUZZbI9YiNjWX37t2sX7+eZcuWYRjGNQtP+UsPZ8yYwYQJE6hevTrdu3cnKiqqwNXmWrZsyYIFC/jnP//J/PnzycnJoVatWrRr1865zfW8HiwWCx9++CFTp07ls88+4/PPPyc4OJj+/fu7XDnNTBZL+Sw8+fnl/T5UqLCVxMStvPWW6+35739Wq5UZM2YwadIkFi1aRFpaGqGhocyYMYMWLVoU6ViDBw+mcuXKfPLJJ6xevZratWszcuRInnrqqStuv3btWg4fPsyUKVNcxlu3bs3o0aOZO3cu6enpdO/eXf2dRCSv8OQo74WncviHTETEg1mM6+n0KyIibm/8eBg1yuwU5rnvPvj8c6hSxewkIiI3aEHx9KMss/60C6o2NjuFiIgUE/V4EhHxML6+Zicwl62cTxQQkTLOuPZVbz2eZjyJiHgUFZ5ERDxMeVxqd6mcnLwlhyIiZZLhuPY2ns6ijygiIp5E7+oiIh7G39/sBOY6dw58fMxOISJyg3Ivmp3AfBa9iYuIeBIVnkREPExwsNkJzJWQAJdd5FBEpOzIOm92AvP5VDY7gYiIFCMVnkREPMytt5qdwFx2O6SlmZ1CROQGZZ4yO4H5fIPMTiAiIsVIhScREQ9zyy1mJzDfb7+ZnUBE5AalHTM7gbmsPmAt51fJEBHxMCo8iYh4mKpVzU5gvjNnzE4gInIDHDZIP2p2CnP5Vgd7ttkpRESkGKnwJCLiYSpVMjuB+Y6V8wkDIlJG2bMgs5xXzv1uAUeu2SlERKQYqfAkIuJhfHzUXPvoUXDoiuQiUtYYNsg8bXYKc/kHA4bZKUREpBip8CQi4mGystTn6dSpvMdBRKRssWjGk98tYNFHFBERT6J3dRERD2OzqfB05gzkaqWGiJQ1Xn6a8eR3C1i8zU4hIiLFSIUnEREPYxgqPJ05k/c4iIiUKRZvyC7nl+X0CwYvf7NTiIhIMVLhSUTEw1itEBxsdgpznT6d1+tKRKRMyU2h3Pc3CgzRUjsREQ+jd3UREQ/j46MZTwkJ4K8vzEWkrMk+Z3YC8wXUNjuBiIgUMxWeREQ8jL+/Zjzl5kJGhtkpRESuU0Y57+8E4F/T7AQiIlLMVHgSEfEwFguEhJidwnwXLpidQETkOqX/anYC8/lWNzuBiIgUMxWeREQ8UG2tVODkSbMTiIhcB0cupMWbncJ8vlXMTiAiIsVMhScREQ9Uo4bZCcy3bRvY7WanEBEpIls6JP1kdgrz+VQyO4GIiBQzFZ5ERDxQda1UYMcOSEszO4WISBF5+UPiD2anMJd3BbMTiIhICVDhSUTEA1WtanYC8/3wA3h7m51CRKSI7JmQlWB2CnP51QB7ttkpRESkmKnwJCLigQICoFI5X61w7BgYhtkpRESKKGm32QnMF/RHMGxmpxARkWKmwpOIiAfKzIS77zY7hfn27jU7gYhIEThy4dxms1OYL6h+3pJDERHxKCo8iYh4IKtVhSeALVvUYFxEygBbOvz2vdkpzFe1sQpPIiIeSIUnEREPVKECNGhgdgrzbd+uBuMiUgaosXieqg3NTiAiIiVAhScREQ9ktcK995qdwnxqMC4iZYI9E7LOmp3CfBX/YHYCEREpASo8iYh4qLAwsxOYTw3GRaRMUGNx8K0GVj+zU4iISAlQ4UlExEPVqAE+PmanMJ8ajIuIW1Nj8TxBd+fN/BIREY+jwpOIiIfKyoK77jI7hfnUYFxE3Joai+epXB8sWhstIuKJVHgSEfFQDgfUr292CvOpwbiIuDU1Fs9TJRK8K5idQkRESoAKTyIiHiowUIUnUINxEXFz9iw1Fgeo2gQsFrNTiIhICVDhSUTEQ/n4QFSU2SnMpwbjIuLWkn4yO4F7CAo1O4GIiJQQFZ5ERDxYRITZCdzDjh1mJxARuQJ7FpxZZ3YK83n5g29Vs1OIiEgJUeFJRMSD3X67Vi4ALFigPk8i4oYMO5xcYXYK81UKBVuG2SlERKSEqPAkIuLB7Ha47TazU5jvs8/U50lE3FBuKqQcMDuF+SrX17ckIiIeTIUnEREPlpOjBuMACQnwyy9mpxARuYTDBieWmJ3CPQTdoyvaiYh4MBWeREQ8mL8/3H232Sncw4IFkJVldgoRkd/Z0uH4YrNTuIdq94LFy+wUIiJSQlR4EhHxYP7+0KyZ2Sncw/LlYLOZnUJE5HdWbzj/jdkp3EMVXQlDRMSTqfAkIuLh2rQxO4F72LsXMtS7VkTcxZkNYKgajm818K9pdgoRESlBKjyJiHi44OC8/wSWLs1ruC4iYqrcFDj2idkp3ENwNNi1DlpExJOp8CQi4uGysiA62uwU7mHxYkhLMzuFiJR7Vj84s9bsFO7h1nbgXdHsFCIiUoJUeBIR8XCVKkH79mancA9btoCPj9kpRKTcS9qdN+tJoNaDYFVjcRERT6bCk4iIh7NaoUMHs1O4h9xc2LTJ7BQiUq7Z0uHXf5mdwj14V4AKfzA7hYiIlDAVnkREyoG6dfNmPgl88gmkaKKBiJjGCqdWmR3CPdzSEuyZZqcQEZESpsKTiEg5kJkJ991ndgr3sGYN+PmZnUJEyq2ss5D+q9kp3EONB/JmPYmIiEdT4UlEpByoWBFiYsxO4R6Sk2HPHrNTiEi55MiBYwvNTuE+ancGq7fZKUREpISp8CQiUg54eUGnTmancB/z5kF6utkpRKTcsefA8UVmp3APVl+oHG52ChERKQUqPImIlBNhYVpilu/jj/OarouIlKrMU5C0y+wU7qFaFNizzE4hIiKlQP/sFhEpJ7KyoFkzs1O4h+RkWLYMbDazk4hIuZGbCvsnmp3CfdS4H7z8zU4hIiKlQIUnEZFyIiAAHnjA7BTuY9IkyM42O4WIlB8WOPZvs0O4j9pdwEvTcEVEygMVnkREyglfX+jc2ewU7mPXLjh2zOwUIlIu2HPg6FywZ5qdxD1YrFDtXrNTiIhIKVHhSUSkHGnUKK/RuOSZOBFSU81OISIez7DB4Slmp3AfVSLByDU7hYiIlBIVnkREypGcHGjc2OwU7uPTT81OICLlQtJuSP2P2SncR437weJjdgoRESklKjyJiJQjvr7Qpo3ZKdxHVhbMmaNeTyJSgnJT4ICairuo3Rm8A8xOISIipUSFJxGRcsTfH3r2NDuFe5kyBRwOs1OIiMdy5MKpz8xO4T6sPhAcbXYKEREpRSo8iYiUMw0bQrVqZqdwH0ePws6dZqcQEY9ky4RDU8Cwm53EfdRsBw6b2SlERKQUqfAkIlLO5OZCt25mp3Av77wDKSlmpxARj2MBjswwO4V7qfcE+FQyO4WIiJQiFZ5ERMqZihXh6afNTuFeVq9WnycRKWYOB5zZAFkJZidxHxYvCIkFiz6CiIiUJ3rXFxEph5o3h8qVzU7hPhwO+PvfISPD7CQi4jHsGXDgPbNTuJcabQA11RMRKW9UeBIRKYeys+Ghh8xO4V5mzQKLxewUIuIxsi/A+a/NTuFe7ugLXhXNTiEiIqVMhScRkXIoKEjL7S53/jwsWwY5OWYnEZEyLzcV9o41O4WbscDtj4DVy+wgIiJSylR4EhEpp6KjoUIFs1O4lxEjwKaLLYnIzcpJgl/mmZ3CvQTfp95OIiLllN79RUTKqZwc+NOfzE7hXk6cgNmzITPT7CQiUmblpsEPr4BhNzuJe6nbF7wCzU4hIiImsBiGYZgdQkREzLFqFcTGmp3CvVSrBseO5V39T0TkuiXvhdUNzE7hfh49D363mJ1CRERMoBlPIiLlWPv24O9vdgr3kpgI774L6elmJxHxTD///DPjxo2ja9euNG7cmOjoaPr378+ePXsKbJuWlsbYsWNp1aoVkZGR9OzZk61btxb5WIZhMHfuXB588EEiIiLo2LEj8+bN4/LvXY8ePUpcXByNGzema9eubN68+Yq5GzVqxIkTJ65+QFsa7Hy5yPnKjepNwepndgoRETGJCk8iIuVYTg48+KDZKdzPe++pybhISZk1axarV68mKiqKkSNH8tRTTxEfH0/Pnj1dCj6GYfDCCy+wePFievTowahRowB4/vnn2bFjR5GO9f777zN+/HgaNmzImDFjiIiI4O2332b69OnObex2Oy+99BJJSUm89tprhISE8PLLL3Pq1CmXLGPHjuXZZ5/l9ttvv/LBHHZI/BHOfXX9D4qnq9sbvPQth4hIeaWldiIi5ZhhwJIl8NhjZidxPwMHwsSJWnInUtx27dpFREQEvr6+zrGkpCS6dOlCzZo1WbZsGQDr1q1j0KBBjB8/nkceeQSA7OxsHnroISpVqsTSpUsLPU5CQgLt2rXjoYceYsKECc7xYcOGsWHDBr788kuqVatGfHw8nTt3Zv78+URFRZGVlUXz5s0ZNWoUvXr1AmDRokVMmzaNNWvW4Od3lZk7tgzY0AqSfrqZh8czdT8DAbeanUJEREyiGU8iIuWYxZLXYNzHx+wk7mfmTEhLMzuFiOdp0qSJS9EJoGrVqjRr1owjR444x9asWUNQUBCxlzSi8/Pzo0ePHuzbt4/jx48XepxNmzaRm5tL3759Xcb79u1LVlYWmzZtAvKKWQCVKlUCwN/fH39/fzJ/v8rAxYsXmTx5MqNGjbp60cmeC2c3qOh0JVUagrcq+CIi5ZkKTyIi5ZzNBjExZqdwP7m58OqrkJpqdhKR8uHcuXNUqVLF+fP+/fupX78+3t7eLttFRkYCsG/fvkL3t3//fnx8fAgPD3cZv+eee7Barezfvx+AevXqERQUxMyZMzl58iRz584lOTnZeZz333+f8PBw2rdvf/WDGbmwa2iRz7VcqdsLrL7X3k5ERDyWCk8iIuVcpUrwxBNmp3BPCxfCmTNmpxDxfDt37mTXrl106dLFOXb+/HmCg4MLbJs/du7cuUL3ef78eapWrYqXl5fLuK+vL1WqVHHePyAggL/85S9s3LiRdu3aMWHCBJ555hmaNGnCwYMHWbJkCa+//vrVD2TPgmOfQFp8UU+3fLnjCfBS4UlEpDzzvvYmIiLiyaxWePhhCAiA31eWyO8MAwYNgsWL1etJpKScO3eOIUOGULt2bV588UXneFZWVoEleYBzuVv+Ermrudr98/eRlZXl/Llz585ER0dz9OhRatWqRc2aNQH461//SlxcHPXq1WP58uXMnj2b9PR0OnXqxJAhQ/JmYxkO2D36us+7XKjaBPyqmZ1CRERMphlPIiKCYUDv3mancE/r1sGBA+BwmJ1ExPOkpqby/PPPk5GRwbRp05x9liCv11LOFS4vmV9wumq/pWvcP38f/v6uV1kLCgqiUaNGzqLTihUrOHHiBAMHDmTnzp2MHDmSp59+mr/97W+sWLGC//3f/81rKH7oA8hKuK7zLjfqDwWrrmYnIlLeqfAkIiJUqgQjRpidwn29/DJcMjlCRIpBZmYm/fv355dffmH69OmEhYW53B4cHHzF5XTnz58HoEaNGoXuPzg4mMTEROx2u8t4Tk4OycnJhd4/LS2Nd999l+HDhxMYGMjy5cuJiori0UcfpUWLFvTq1Yvly5eDYYf9fyvqKZcvPlXgtu5g9br2tiIi4tFUeBIREQBq14amTc1O4Z62b4eNG+EaK3tEpIhycnJ46aWX2L17N++//z5RUVEFtgkPD+fgwYPYbDaX8d27dztvL0x4eDg2m83ZRDzf3r17cTgchd5/6tSp1KtXz9lz6uzZsy6Fqho1apCQcBZ+fhNyUwo/2fLqzn6ApoqKiIgKTyIi8ruAABiqizJd1Z//rFlPIsXBbrczbNgwtm3bxsSJE3nggQeuuF3Hjh1JSUlh5cqVzrHs7GyWLFlCeHg4devWdY4nJiYSHx9P5iWN6tq1a4ePjw/z58932e+CBQvw8/Mj5iqX84yPj2fBggW88cYbzrHg4GCOHj36322O/Ifg6pXh8JTrOvfywwJ3DwXvCmYHERERN6Dm4iIiAoCXF8TGQrVqkJhodhr3c/48PPcczJmjRuMiN2PixImsW7eOVq1aYbfbWbFihcvtHTp0IDAwkI4dO3Lvvffy1ltvcezYMWrVqsWyZcs4deoUc+bMcbnP/Pnz+fDDD5k3bx7NmzcHoGbNmjz77LNMnz4dh8NB06ZN2b59O6tWrWLQoEFUr179ivnGjRtHz549CQ0NdY517tyZpUuXMmbMGEJCQlj46SL6RWflNRaXgmrGgE+la28nIiLlggpPIiLiZBjw7LPw7rtmJ3FPixbBU09B+/Zwjb7GInIVBw4cAGDr1q1s3bq1wO2bNm0iMDAQq9XKjBkzmDRpEosWLSItLY3Q0FBmzJhBixYtinSswYMHU7lyZT755BNWr15N7dq1GTlyJE899dQVt1+7di2HDx9myhTXmUytW7dm9OjRzJ07l/T0NLq3vpWBzTde55mXI+H/A94qPImISB6LYRiG2SFERMR9nDkDISF5RSgpKDgY/vMfqFzZ7CQiUuocNkjeA+uiNNvpagJCIPYIeOlqdiIikkc9nkRExEWFCtChg9kp3Ff+kru0NLOTiEipc+TA1p4qOhUm9EWzE4iIiJtR4UlERFwEBcH//I/ZKdzbokWwebOucidSruSm5V3FLvWI2Uncl9UH/jhAs51ERMSFCk8iIlLAffdBnTpmp3Bv/frpKnci5YbDDqn/gUN/NzuJe7vtYbDo44WIiLjSXwYRESnAaoUXtVqiUFpyJ1KOOLK1xK4owoeDT5DZKURExM2o8CQiIgX4+UH//uDra3YS96YldyLlgJbYFU3lcAiqb3YKERFxQyo8iYjIFVmt8MgjZqdwf1pyJ+LBtMSu6MJezevxJCIichkVnkRE5IoqVYLhw81O4f4MAzKzDAzDMDuKiBQ3LbErGu8KcEcfFZ5EROSKVHgSEZGr+uMfITLS7BTuq1cvOH7KINEnm19Sc7E5VHwS8Rg2LbErsjvi8qrwIiIiV2Ax9BWtiIhchd0Oa9ZA165mJ3E/S5cZdOkKa0+ksT8pGz8vC8/eXYWKPlasFovZ8UTkZtgy4dyX8NVDgP6pXCiLNzx8EgJqmp1ERETclGY8iYjIVXl5QUwMNGhgdhL3ERYGCRcMojvamXMwif1JeZ3Fs+0G/z6SollPImWdwwaZp+CbnqjoVAR3Ppu31E5EROQqVHgSEZFC+fnBu++ancI9DB0KP+81+NWexUeHkknOce37kphtZ9kvqeSq+CRSdtnS4Iv2YEs3O4n7s/pC5DjwqWh2EhERcWPeZgcQERH35uUFrVtD48bw449mpzGHvz98tcUgsrHB0l9T+TU196rb/pKayzdnMmh1ayC+XlpyJ1Km2DJgcyykHzM7Sdlw53Pg5W92ChERcXOa8SQiItfk7w/vvWd2CnPcfz+c+82g1t25zDyQVGjRKd/2c5nEp2STY9eVsETKDFs67BoK5782O0nZ4OUPkWM120lERK5JhScREbkmqxWaN4eoKLOTlK4pUww2fGGwIymdBUdSyLAVfQndZ8fSSM5xYNeyOxH3Z0uHXz+BI9PNTlJ23PVC3lI7ERGRa9BV7UREpEgcDti6NW8GkKerUQO2fWcQHOJgydEUEjLtN7SfCt4W/ly/KgHe+p5HxG3ZsyF5N6xvBYbN7DRlg1cAdD8NvlXMTiIiImWA/iUsIiJFYrVCkybQsqXZSUpWnz5w7IRBVuVsZh5IuuGiE0C6zWBhfAq5dn3HI+KWHA7ISYKvOqvodD1CXwarj9kpRESkjNCMJxERKTLDgO++g/vuMztJ8bNaYekygz91gTUnUjmQlFNs+w6v6sefbq+Ij5qNi7gXWzqsaw4X95mdpOzwrpA328knyOwkIiJSRmjGk4iIFJnFApGReVe58yT168PZ8wYt29uZfTCpWItOAPuTsvnxtyw1GxdxJ7Z02Pq4ik7XK2wwWLzMTiEiImWICk8iInJdAgNh8mSzUxSf//kf+Olng6O5mcw9lMzFnJIpDn15Kp3TGTZsajYuYr7cNDjwLpxaaXaSssW7EoSPyJv1JCIiUkQqPImIyHWxWODuu6FtW7OT3JzAQNjxvcFbbztY8ksKX57OoCTnIxnA0qOppOY6VHwSMZMtHc5ugD1/MTtJ2VN/qGY7iYjIdVOPJxERuSE//QSNG5ud4sY88ACs/NzBbzYbK46lkmkrvT+Fgd4WngqrQkVvK15W9XwSKVW2DDi3BTZ3VTPx6+VTGR4+CT4VzU4iIiJljGY8iYjIDbnrLujQwewU12/qVIN1Gw22J2bw7/iUUi06AWTYDD4+fJEMuwO7Zj6JlB5bBvy2A7Z0U9HpRoQPB4s+OoiIyPXTjCcREblh+/ZBRITZKYrm1lth63cG1W/NW1p3LtNuap4gHytPhVUhwNuC1aKZTyIlypYJyT/BphiwZ5mdpuzxrQYPH1dvJxERuSH62kJERG5Y3brw+ONmp7i2vn3hl+MGGZWymXUwyfSiE0BKroOPDyeTZTdw6DsgkZJjy4SU/bCpvYpON6rxu+rtJCIiN0wznkRE5KYkJUG9enDxotlJCrJaYcVKgw6dDNYcT+Ngco7ZkQqo5ufFk6GV8fXSzCeRYmfPgtQjsP4+sKWanaZsqhYF7TeDd6DZSUREpIzSjCcREbkp/v7w3ntmpyjonnsg4bxBs7Z25hxMdsuiE0Bitp2PD18kWzOfRIqXLRNSDsGGVio63SiLFVp+DF4BZicREZEyTIUnERG5KQEBecvtmjQxO8l/jRwJu34yOJKbydxDyVzMcZgdqVC/ZduZdziZLJuBQw3HRW6eLRMu7oUN0ZCbYnaasuuPL0LgbaDZmCIichO01E5ERG6aYcCBA9CgAThMrPEEBsKWbwzCGxgs+yWV42m55oW5AZV9rcSFViHAy4KXVR/0RG6ILQOSdsEXD4I90+w0ZZd/Teh6BHwqmp1ERETKOM14EhGRm2axQJ06MGCAeRnatYOECw6q3ZnL/+5PKnNFJ4CLOQ7+71Ay6TYHds18Erl+tgy48O3vjcRVdLopTaeB1cfsFCIi4gE040lERIpNWhrcdRckJJTucadNM3jmOdh8Jp0fzpf9q1ZV8LYQF1qFij5WvDXzSaRobOlwbjNseRgcZa/w7FZqxkCbleBdwewkIiLiAVR4EhGRYpOTA6tWQY8epXO82rVh67cGVWs6WPJLCucy7aVz4FIQ4G2h151BVPfzxsdLxSeRQtnS4fgi2P5nMDznfcAUVl+Ijc/r7SQiIlIMtNRORESKja8vdOoEDzxQ8sd68kmI/9UgtUI2sw4meVTRCSDTZjDv8EUOJWeTY9d3RCJXZcuAH/8HvuunolNxCB8BvlXNTiEiIh5EM55ERKTYHT+et+QutwRWu1itsOozg3YPGqw+nsah5JziP4ibufcWfx4IqYCPlt2J/JfDljfTacvDcO4rs9N4hgr1oMte8A40O4mIiHgQzXgSEZFiV706jBhR/PuNiIBzvzlo0sbO7APJ5aLoBPDDhSwWx6eQbXfgMPH7op9//plx48bRtWtXGjduTHR0NP3792fPnj0Ftk1LS2Ps2LG0atWKyMhIevbsydatW4t8LMMwmDt3Lg8++CARERF07NiRefPmcfn3ZUePHiUuLo7GjRvTtWtXNm/efMXcjRo14sSJE9d/0uKebJmQfhzWNFbRqTi1mJO31E5ERKQYacaTiIiUiIwMuOce+PXX4tnf66/DG28Z/HA+ky1nMiiPf7yq+FrpfVdlKnhbTen7NGjQIHbu3EnHjh2pX78+Fy9eZOHChZw6dYrp06fTpk0bIK9oFBcXx88//0y/fv2oVasWS5cuZd++fXz00Uc0a9bsmsf6xz/+wbRp04iNjaVZs2Z89913fPbZZ7zyyisM+P3yiXa7na5du2K1Wnn88cfZsmUL27ZtY82aNYSEhDizPPbYY7Rp04aXX3655B4cKT25aXBhK3zdA2xpZqfxHLc9DC0/Bp+KZicREREPo8KTiIiUCJsNtmyBdu1ubj8VK8KWbwzCwg2W/5rK8bTyfbUqX6uF7vUqEVLBB99SLj7t2rWLiIgIfH3/OyMiKSmJLl26ULNmTZYtWwbAunXrGDRoEOPHj+eRRx4BIDs7m4ceeohKlSqxdOnSQo+TkJBAu3bteOihh5gwYYJzfNiwYWzYsIEvv/ySatWqER8fT+fOnZk/fz5RUVFkZWXRvHlzRo0aRa9evQBYtGgR06ZNY82aNfj5+RX3QyKlzZYOh6bA7tFQLsvPJcS7AsT+Cv63mJ1EREQ8kJbaiYhIifD2hmbNoFu3G99Hhw5w5pxB5Xq5/O+BpHJfdALIcRgsjE9h14VMch2l+8G7SZMmLkUngKpVq9KsWTOOHDniHFtZX1S7AAAgAElEQVSzZg1BQUHExsY6x/z8/OjRowf79u3j+PHjhR5n06ZN5Obm0rdvX5fxvn37kpWVxaZNm4C8YhZApUqVAPD398ff35/MzEwALl68yOTJkxk1apSKTmWd4cgrOn37NOwehYpOxazh39TXSURESowKTyIiUmIqVoRZs+D3usB1mTnT4LM1BtsupPNpfApZurKbi69OZ/D5sVRy7UaBvkel7dy5c1SpUsX58/79+6lfvz7e3t4u20VGRgKwb9++Qve3f/9+fHx8CA8Pdxm/5557sFqt7N+/H4B69eoRFBTEzJkzOXnyJHPnziU5Odl5nPfff5/w8HDat29/0+coJrLnQPZvsCEaTiw2O43nqRIJd/5ZhScRESkx3tfeRERE5MZVqJBXfPp95dM11a4N27Y7qBxs8PHhFM5n6fLoV3MwOYek7GR63VUZPyt4mXDVu507d7Jr1y6efvpp59j58+dp0KBBgW2Dg4OBvEJVYc6fP0/VqlXx8vJyGff19aVKlSrO+wcEBPCXv/yFUaNGsWrVKiwWC8888wxNmjTh4MGDLFmyhOXLl9/kGYqpbOmQchi+6gRZhb9u5AZ4+cP9y/P+LyIiUkJUeBIRkRIVEABdukDPnvDpp4Vv268f/HOGwX9Sclh0IA2bJjldU0KmnVkHkuh1Z2Wq+lnx9Sq9ycznzp1jyJAh1K5dmxdffNE5npWVVWBJHuBc7pa/RO5qrnb//H1kZWU5f+7cuTPR0dEcPXqUWrVqUbNmTQD++te/EhcXR7169Vi+fDmzZ88mPT2dTp06MWTIkAKzscQN2dLhxBLY/mdwaJltiWjyd/CvCRYtghARkZKjf3WJiEiJq1ABZs6Ebdvg5MmCt1utsHqNwQPtDFYfS+PQxZzSD1mGZdgM5h5KpkXNAO67NRAvC1gtJTv7KTU1leeff56MjAzmz5/v7LMEeb2WcnIKPof5Badr9Vu62v3z9+Hv7zo7IygoiEaNGjl/XrFiBSdOnGDmzJns3LmTkSNHMm7cOEJCQhg6dCgVK1Zk4MCBRT5XKWX2LLBnwnf94OQKs9N4rloPQr04LbETEZESp683RESkVAQEwOLFcHk9JDISzv3moGErG7MPJKvodIMM4NuETOYeTOa3LDs5dkeJHSszM5P+/fvzyy+/MH36dMLCwlxuDw4OvuJyuvPnzwNQo0aNQvcfHBxMYmIidrvrMsucnBySk5MLvX9aWhrvvvsuw4cPJzAwkOXLlxMVFcWjjz5KixYt6NWrl5bfuTNbOpxeDSvvUtGpJPndAvd9knc1OxERkRKmwpOIiJQKHx+45x4YPvy/Y2+8Ad/vMjiUlcX/Hb5ISm7JFUvKi9+y7cw5mMy3CXlXvXMUc+PxnJwcXnrpJXbv3s37779PVFRUgW3Cw8M5ePAgNpvNZXz37t3O2wsTHh6OzWZzNhHPt3fvXhwOR6H3nzp1KvXq1aNLly4AnD171qVQVaNGDRISEgo/SSl99izISYJtfeHrRyEn0exEnu2+f6noJCIipUaFJxERKTUVK+YVm1q1gp9+NhjxhoNF8RfZciZDF0cvRs7ZT4eKd/aT3W5n2LBhbNu2jYkTJ/LAAw9ccbuOHTuSkpLCypUrnWPZ2dksWbKE8PBw6tat6xxPTEwkPj6ezMxM51i7du3w8fFh/vz5LvtdsGABfn5+xMTEXPG48fHxLFiwgDfeeMM5FhwczNGjR122yW9yLm5Cs5xK153PwS2twKvwJa8iIiLFRT2eRESkVAUEwKbNDk6l2fjfA6lk2VVyKim/ZeXNfiqu3k8TJ05k3bp1tGrVCrvdzooVrkWCDh06EBgYSMeOHbn33nt56623OHbsGLVq1WLZsmWcOnWKOXPmuNxn/vz5fPjhh8ybN4/mzZsDULNmTZ599lmmT5+Ow+GgadOmbN++nVWrVjFo0CCqV69+xXzjxo2jZ8+ehIaGOsc6d+7M0qVLGTNmDCEhISxcuJB+/frd8GMgxUi9nEpfpT/CvX/XbCcRESlVKjyJiEipsvxe/Mi0O1R0KgX5s58OX8zh4TsqUdn3xq98d+DAAQC2bt3K1q1bC9y+adMmAgMDsVqtzJgxg0mTJrFo0SLS0tIIDQ1lxowZtGjRokjHGjx4MJUrV+aTTz5h9erV1K5dm5EjR/LUU09dcfu1a9dy+PBhpkyZ4jLeunVrRo8ezdy5c0lPT6d79+5qLO4ObOlwZh1sf07L6kqL1Q8e+Bys/tfeVkREpBhZDKOYmz+IiIgUQY7dYP2JNPYmZZsdpdywQKle+U6kAM1yMk+LOVCnl65iJyIipU6FJxERMU2O3WDuoWQSs+3X3liKTXV/r99nP3nh66Xik5SS3DQ4u16znMxQpxe0mK0ldiIiYgoVnkRExDQOw+BijoPZB5Kw6a9RqbIADar50TakAl4WiwpQUnJyUyHzNOx8Cc5uNDtN+VPxTvjTT+BT0ewkIiJSTumqdiIiYhqrxUJFHwud6ugDUWkzgJ8Ts5m6N5FtZzPIsTvIdaj6J8UoNw0yTsP2Z+Gz+io6mSG/r5NXgNlJik1MTAwjRowwO8Y1ffvttzzyyCM0bNiQsLAwZ488T3Dy5EnCwsJYunRpiR1j6dKlhIWFcfLkyRI7hlni4uKIi4szO4bptm/fTlhYGNu3bzc7ipQCFZ5ERMRUPlYroZX9CK+qS3ubwWbAd+cymboviV3nM8l1GNhUgJKbkZuet5Tux2Gwoi4cX0ReqVNKXdSHEHg7WL1KZPf5xYEr/Veem/inpKQwaNAgDMPg9ddf55133qF27doFtps3bx5hYWFs2LDhqvs6ePAgYWFhTJgwoUSyLl26lHnz5pXIvuXmxMXFERYWdtUiVXh4eJkowt6s/ELnpf81btyYzp07M3XqVDIyMko8g35Pbp6uaiciIqbz9bLQ6faKnMu0cSFL/Z7MkG03+PJ0Bt+fy6JN7UDuruqHFfCyagmeFJEtEww77B8PB/+e10RczFO3D9zRp1SaiQ8cOJA77rjDZaxWrVolcqy1a9dicfMLI+zdu5eUlBRefPFF2rdvf9XtunTpwsSJE1m5ciUdOnS44jYrVuQ14e/WrVuJZF22bBlnz57lySefLPJ9QkJC+Pnnn/H21kfJ0rBjxw6+/fZbWrZsaXYUU3Xs2JF27doBkJGRwc6dO5kyZQp79+5l2rRpJXrsG/k9EVd6txAREbfgY4U+d1Vm9sEkMtTwyTRpNgefH09jW0IG7UIqULeSr66AJ4WzZ+cVnI7MgL1/hZwksxPJLS2h+axSu4Jdq1atiIqKKpVj+fr6lspxbsZvv/0GQKVKlQrdrnr16kRHR/PVV1+RkpJCUFCQy+0Oh4PPP/+c0NBQ6tevX2J5iyo3NxfDMPD19cXPr2zNUs7KysLX1xertWwt+KlRowZ2u50PPvigRAtPhmGQlZVFQID7Lsu9++67XQqwffr0ITc3l/Xr15OWlkbFimrb4M7K1m+eiIh4LIvFgr+3hT53VcZbNQ7TJWU7WHw0lX8dTuZ0uo0cu8PsSOJuHLa8WU7HP4VVobBriIpO7qBCPXhgdakVna7lav1sPvjgA8LCwlzGvvvuO+Li4mjevDmRkZG0bduWYcOGuSyluVKPp+TkZN566y2io6OJiIigU6dOzJ49G4fD9X0rLCyM0aNHs3nzZh5++GEaNGhAhw4dWLVqVZHPZ/HixcTGxtKgQQNatGjBa6+9xtmzZ13Od9iwYQA8+eSThS6VgryZTDk5Oaxdu7bAbdu3bychIYHY2Fjn2Jo1a+jZsycNGzakSZMmPP/88xw6dMjlfiNGjCA8PJwLFy4wePBgmjRpQtOmTRk9ejRZWVnO7WJiYtixYwfHjx93LmGKiYlxHjssLIzly5czbdo0YmJiiIyMJD4+/qo9ni5cuMBf/vIXHnjgASIiIrj//vsZOnQoCQkJLvu8Uk+fsLAwPvjgg6s+TgA7d+7k1VdfJSYmhoiICFq2bMmwYcNcHn/47xLQbdu2MX78eKKjo2nUqBFpaWlX3ffSpUvp16+f8zXUrl07Jk2aRE5Ozg09tpBXzJk5cyZt27YlMjKSXr16sWvXrkLP8XL+/v4899xz/PDDD3zzzTfX3P56fxfWr19Pt27daNCgAQsXLnR53vOzN2zYkGeeeYbTp08D8NFHHxETE0ODBg144oknOH78uMu+i/o8FYfg4OD/b+/Oo6Oq7/+PP+8s2TcSSJBAIAgJu1BZhQpEQAQREWRTFFARqrUqilCttlas0EJPQRGQTQVFCCAoiIAiKmWRH4oUF4QomxqFkITsmZn7+yNmvgxJIIEMk5DX45wcyJ07977vvXMzk1c+C6ZpYrV6dic+evQoEydOpHPnzrRq1Yqbb76ZFStWeKxTUFDArFmzGDJkCB07dqRNmzYMHDiwxOv6fPeJlJ9aPImISJVhNQwi/K3cGh9Gckqmr8sRIDXXydLvMmgYYqd3g2BC7Rb8rfq7VY3mcoArH375GPY+ApnfXvg5cnnYI6DXVrCdv6VNZcvKyiItLc1jWXh4eIlfBs/n8OHDjBs3jiZNmjBhwgSCg4P58ccf+fDDD8nOziYoqPQgraCggLvvvpvvvvuOYcOG0aRJEz7++GOmT5/OiRMnePrppz3W379/P1u3bmX48OEMGTKElStXMmnSJFq0aMHVV1993hrnz5/PjBkzuPbaa3n88cdJTU3l9ddfZ8+ePaxZs4aIiAjGjx/P1VdfzZtvvsn48eNp3LgxtWvXLnObSUlJhISE8M477zB06FCPx9atW4fFYnEHTwsXLmT69On07t2bgQMHkp2dzfLlyxkxYgSrVq0iPj7e/VzTNLn33nu5+uqreeyxx9i/fz/JyclERkYyceJEAP785z8zY8YMMjMzmTRpEgDBwcEeNSxYsACXy8XIkSOx2WyEh4eXCDGgKHQaOnQov/zyC0OGDKFZs2akpaWxbds2jhw5QkxMzHnPbXls3LiR9PR0hgwZQu3atfnhhx946623+PLLL1m3bh0BAQEe6z///PMEBwdz3333kZeXh91uL3Pby5YtIz4+nm7duhEUFMTnn3/OggUL+PHHH5kxY4bHuuU5twAvvfSSu6XSPffcww8//MD48eMJCwurUFfUESNGsHDhQmbPnk23bt3KXK+i98Lnn3/Oli1bGDFiBCNHjvToLvvqq68CMHr0aNLS0li4cCEPPvggvXv3ZvPmzdx1112cPn2ahQsXMmnSJJYvX+5+bkWvU3nl5eW5f87k5OSwd+9e1qxZw0033eTRUuv7779n+PDhhIeHM3r0aMLCwvj444/5y1/+Qnp6OuPGjQOKfm4tX76cfv36MWjQIAoLC/nggw+YMmUKDofDfT+W5z6RC1PwJCIiVYrdYhAXYiepXjAf/pjt63LkN0eyClnwdTrxoXY6xQQSG2zHAGwaA6rmKMwGw1LUwumbmZD+pa8rkrNZ7NDzPQio67XBxMty//33l1i2YcOGCwY5Z/v000/Jz89nwYIFREZGupf/6U9/Ou/zVqxYwTfffMPf/vY3hg8fDsDIkSN5+OGHWbZsGSNGjKBp06bu9Q8dOsTbb79NQkICAH379qVHjx4kJyfzxBNPlLmftLQ0Zs+eTfv27Xn11Vfd4xt16NCB+++/n/nz5zNp0iS6du3KyZMnefPNN7nuuuvo1KnTeesPCAjgxhtvZPXq1fz000/uQCIvL49NmzbRqVMnYmJi+Omnn5g5cybjx4/nkUcecT//tttuo1+/frz44oseAYnL5aJbt27u1lcAGRkZrFy50h2O9OrVi1dffRWHw1HmGFIZGRm89957Ht2YSptpbsaMGZw4cYJXX32Vzp07u5f/4Q9/wDQrp/v8xIkTS3QFS0pK4o477mDTpk0eLcOgqFvm0qVLzxs4FVu6dKnHtkeMGEGjRo2YNWsWjz/+OHXr1nU/Vp5zm5aWxty5c+nUqRMLFy50h7BNmzbl6aefrlDwFBAQwLhx45g6dSrbtm2je/fupa5X0XshJSWF5ORkWrVq5V5W3BotKyuLd999192l0uFwsGDBArKzs3nnnXfcXV6Llx8+fNh9v1f0OpXXvHnzmDdvnseyG264gWnTpnkse+6554iIiGDNmjXuwHrkyJFMnDiROXPmMGLECEJDQwkPD+ejjz7y6L47evRoRo8ezYIFC9zBU3nuE7kw/clSRESqHD+rQbvaAbSJrF5jSNQE358pZPmhTF756jR7fskl3+kiX93wrlymCwrPQPYx2DcF1tSFnaMVOlVFnRZDRGuwXv6fm1OmTGHx4sUeX6XN4nY+xeMbbd68Gaez/JNMbN26lfDwcAYPHuxeZhgG99xzDwAfffSRx/rt27d3h04AtWvXpnHjxqWGKWfbsWOHu0XJ2YNq9+jRgyZNmpTYT0UMHDgQ0zQ9uvx9+OGHZGVluX/Rff/993E4HPTv35+0tDT3l8VioW3btqV2Xxs5cqTH9x07duT06dPn7XJWWm0XGjvH5XKxefNmunbt6hE6FausweDPDjOKW9k1btyYsLAwDhw4UGL922+/vVyh09nbdrlcZGZmkpaWRocOHTBNs9RtX+jc/ve//6WwsJBRo0Z5tPy77bbbLjj2V2mGDx9OTEzMebsjVvReaNOmjUfodLZbb73VYxyvdu3aAXDLLbd4BDXFy8++fyp6ncrrtttuc/98eemll7jvvvv49NNPeeyxx9zhZkZGBtu3b6dv377uFlLFX7///e/Jzc3liy++AMBqtbqPpbCwkPT0dNLS0ujSpQtHjhzhzJkzF12rlKQWTyIiUiXZrQa9G4SQUeDiSFahr8uRc2QWuvjopxw++TmHhAg/OkcHEelvxWJoJrwrgjMPMCF1K3z1T/jlI19XJOfT8imoPxBsvun+0apVq0seXLxfv36sXr2ap59+mn/961907NiRHj160L9//zK72QGcOHGCuLi4EgFDkyZNgJKtc2JjY0tsIzw8nPT09PPWd+LECQAaN25c4rGrr776koKnjh07Uq9ePd555x13N6C1a9cSGBhInz59APjhhx8AGDBgQKnbOHfQbIvF4tFKB/4v3MvIyCj3QMxxcXEXXCctLY0zZ854BHrekJqayj//+U8++uijEqFAZmbJ7vnlqb3YF198wcyZM/n8889LjOt07r7Kc26Lx0M6u/sjgN1up0GDBuWuq5ifnx/3338/zz77LB9++GGpYwxV9F44Xx3nBsfFYVlZyzMyMtzLKnqdyqtBgwZcd9117u979epFZGQk06ZNY+vWrSQlJXHkyBFM02Tu3LnMnTu31O0UD/4PRbPVLVq0iEOHDpXoQnrmzJmLCgmldAqeRESkyrJbDAY3DuXVbzM4lV/+v4DL5eM04evTBXx9uoDoQCsd6gTSrJZ/0cxHGguq+inMLJqh7uBL8N3LkPujryuSC2kwBFpOqTKDiZfXua2a/P39ee211/h//+//8cknn7B9+3aeeuopXn75Zd566y3q1KlTKfutirOaGYbBgAEDmDdvHl9//TUxMTF8+umn9O3b1z2WTPEvxfPmzSvXzH6GYZR5rBXp+laZs9eV1fKpPC3cXC4XY8eO5eTJk9xzzz00adKEoKAgDMPgkUceKfWYylv7sWPHuPvuu4mLi2Py5MnUq1cPf39/UlNTmTx5colAorLObUXdfvvtvPLKK7z44ouVMrj1+cZaKuv4LnTcF3OdLkXxTH+fffYZSUlJ7ms1atSoMs9RcRC3YcMGJk+eTI8ePRgzZgxRUVHY7Xa2bdvGkiVLSh3LTC6egicREanS7BaDEU3DWfTNaXIc3vtAJ5ful1wn649mseV4Ni0j/ekcE0iA1cBmMbBUUlcL8QJXYdFXxgH4ahocXwumw9dVSXlEdYQuS6p06BQeHs6xY8dKLC9uQXQ2wzBo37497du355FHHmHbtm2MGzeOt956iwcffLDU7cfGxvK///0Ph8Ph0QXu8OHDANSvX79SjqO4pVRKSor7F9diKSkpl7yfgQMHMm/ePNatW0dsbGyJ8WQaNmwIQN26dWnWrNkl7auyRUZGEhISwsGDB8+7XnGroHNbwZT2WjjXwYMHOXToEC+88AKDBg1yL8/Ly7ukVjRQ1K0xLy+PuXPnerSIK88scmUpbhn0/fffe7xeCgsLOX78+EVdQz8/P8aPH88zzzzD5s2bSzx+ue6F8/HmdSqNw1H0XlU882VxKzer1erROqo069evp379+sydO9cjFN25c2el1yka40lERKo4wzAItBoMuzocq7KLaiHfZbL3ZB5zDpwmOeUMKZkFOFwm+U6XV/8aLBXgckBhBjiy4PAi2Nge3u8Ix1YpdKoughtCz40+615XXnFxcRw+fJiTJ0+6l6WmprJlyxaP9U6fPl3iuS1atADO3z0nKSmJ9PT0ElOgL1q0CICePXtedO1nu+666/Dz8+O1115z/7ILsG3bNr777rtL3s/VV19Ny5YtWb9+PW+//TZ16tSha9eu7sf79OmDzWZj9uzZpbbEOHdWwfIKCgq65EDAYrHQp08ftm/fXuov7cU/9+vXr4/Vai2xztKlSy+4j+Jg4NxjX7Ro0SW3TCluxXP2dlwuF4sXL77obV533XXY7XZef/11j+2uXr36ks734MGDiY2NLXWsp8t1L5yPN69TabZu3QrgDvIiIyPp0qULycnJpQaaZ98npV3306dPs2rVqhLPq4z7pKZTiycREanyrBaDSH8LtzYKZdX3GuyxOjmaVcjRrELsFmgU6kfzCH+uDi8af8JmMbCqJdRl43SZWAwTIy8Vjq4sCpl+3V7UtU6qF3sY3PAR2MJ8XckF3X777SxevJixY8cydOhQMjMzefPNN4mPj/cYaHjOnDns3LmTnj17EhsbS05ODqtXr8ZqtXLTTTedd/srVqzgr3/9K99++y2NGzfmk08+YevWrdxxxx0lWiddrFq1avHHP/6RGTNmMHr0aG688UZSU1N5/fXXqVevHvfdd98l72PgwIE8//zzpKamMnr0aI9BqRs0aMDjjz/OP/7xD4YMGUKfPn2oVasWP/74I5988gkJCQm88MILFd5ny5Yt+eijj5g2bRotW7YkKCjoorpxPfroo/z3v//l3nvvZciQITRr1oyMjAy2bdvGww8/TMeOHQkJCaF///688cYbGIZBfHw8u3btuuDA7lAUzDVq1Ihp06bx008/ERUVxe7du/n888+JiIiocL1n+/3vf+8eQ2nYsGGYpsl7771XYqynioiMjOS+++5jzpw5jB07ll69enHkyBHWrl17UWM8FbPb7UyYMIGnnnqqxGOX6144H29ep2+++Ya1a9cCkJuby759+3j77bdp1KiRR+vAZ555hhEjRjBw4EBuv/12GjVqREZGBl9//TVbtmxh//79QNGMeJs2beL++++nV69enDp1ihUrVhAdHe0RlEPl3Sc1mYInERGpFuxWCw1D/ehRL4iPfszxdTlSQYUu+C6jgO8yCjCAesE2EsL9aF7Ln0Bb0V8d7RqUvNIVOE0sBqTlOzmQlk/3aDC23gTp+3xdmlwswwY91kNgXbBYL7y+j8XHxzNjxgz+85//8MILLxAXF8eUKVM4fPiwR/B0ww038PPPP7N27VpOnTpFWFgYLVq04Omnn3bPnFUaPz8/Xn31Vf7973/z/vvvk56eTmxsLI8//jhjx46t1GMZN24ctWrV4rXXXmPatGkEBwfTu3dvHnvssUv+pRrg5ptvZvr06TgcjlKnnB89ejSNGjVi0aJFzJ8/H6fTSXR0NNdeey3Dhw+/qH2OGTOGlJQUkpOTWbRoEbGxsRf1C3WdOnVYuXIls2fPZsuWLSQnJxMVFUWHDh3c3QQBnnzySRwOB8nJyVgsFnr06MErr7ziHqunLDabjXnz5vH888+zZMkSTNOkQ4cOvPbaa4wePbrC9Z6tUaNGvPzyy8ycOZOZM2cSEhLCjTfeyPDhw8sczL08HnroIQIDA1m2bBnTp0+nWbNmzJ07l3//+9+XVO+gQYOYP38+R48e9Vh+Oe+FsnjzOr3//vu8//77QFFXupiYGIYNG8ZDDz3kMQFBfHw8q1ev5qWXXmL9+vWkpaURERHB1VdfzeTJk93r3XrrraSnp7Ns2TKmTp1KvXr1GDt2LKGhoUyZMsVj35V1n9Rkhqk27yIiUo0Uukw2Hctif1q+r0uRShLhZ6FpuB8tI/2pHWDDaZr4a2Dyi+IyTQpcJjbD4FhWIQdO53M4s4Dc38ZHG900iJgj/8HY92cfVyoXrfNiiLu9ynexExERKabgSUREqp1Cl8nqlEy+P1Po61KkkgVYDRqH+dGilh8NQ/1wmiZWDOwa4KtMBc6i8SmcJnyXXsA3GfkcOVOIs5RPeK0j/elXOx1jbfmnGZcqpNVfoPkTYFfoJCIi1YeCJxERqZYUPl35LAZcFWSjbpCNBsF2rgq2EWKzUFiDw6gCpwsTsBkG6QVOjmc5OJFdyE85Dn7NK99YTU+0DsTY2AEyv/ZusVK5Wj0FLSarpZOIiFQ7Cp5ERKTaUvhU89gtEB1YM8KoskKmn3MdnMx1crHzA92TEEjtlH9i7P9bZZYr3qTQSUREqjEFTyIiUq0pfJKywiiHaWKaYBhF4Y21Cg5e7nCZOE0TE7AA1koOmUrzu9r+9I74BeMd789wJJWg1dPQYpJCJxERqbYUPImISLWn8EnOZbdAmN1KiN3i/grzsxDhZyXUz0KwzULAb62jzg6orIaBrRICKofLxGGaYILFMLBZisZgynW4yC50kVnoIj3fyZlCF1lnfWUWuCo1ZCqNBXi8dRDGhtaQleLlvcklUegkIiJXAAVPIiJyRVD4JBfDz2J4hFPFAZWfxcD6WxBl8fi3KKBymUVfTrNoJrmz/811FAVLWYVFIdOZ3/51VKFPXOMSAqj13XMYX03zdSlSFoVOIiJyhVDwJCIiV4a34xcAACAASURBVAyFTyLl0zE6gJ4hxzHWN/d1KVKaVs9Ai8cVOomIyBXB4usCREREKovdYnBb4zDiQ+2+LkWkStv7Sx4EN4SgBr4uRc6l0ElERK4wCp5EROSKovBJ5MIcQGaBAzNusK9LkbMpdBIRkSuQgicREbniKHwSubAvMywQP9rXZUix1n9V6CQiIlckBU8iInJFUvgkcn6f/ZILYYkQEOPrUqT1X6H5YwqdRETkiqTgSURErlgKn0TKVuCCrIJCzPq3+bqUmk2hk4iIXOEUPImIyBWtOHxKCPfzdSkiVc6BDCtcPcbXZdRc1/xDoZOIiFzxFDyJiMgVz24xGNAolM7Rgb4uRaRK2ZmaAxFtwD/K16XULBZ/+P1qSPyjQicREbniKXgSEZEawW4xuK5uEP3jQvTmJ/KbPBfkFORD7EBfl1Jz+NeGPjvgqhsVOomISI2gz94iIlJj+FkNmkX4M7JpOP4Ww9fliFQJX52xYTZWd7vLIiwR+n0J4S3BFuTrakRERC4LBU8iIlKj2K0GdYNsjGkWQbif3gZFdqbmQlR7sIf7upQrW0xPuHE3+MeAVWPOiYhIzaFP3CIiUuPYLAZhfhbGJEZQL8jm63JEfCrbYZJXkA+xA3xdypWr8Vjo/i7Yw8Cij98iIlKz6J1PRERqJIthEGCzMKJJOM0i1PpAarZvsv0wG4/2dRlXIAPa/Qvaz1LXOhERqbEUPImISI1mtxr0bxhK1xjNeCc1147UXKjTVYNdVyZrAHRfB03H67yKiEiNpuBJRERqPLvFoFNMELc0DEFjjktNlFngoqAwH+r183UpV4aAaLjxM4i5QaGTiIjUeAqeREREKJrxrmm4P6OahhNgVfokNc/BbH/M+Lt9XUb1F94S+u0vmsHOppaUIiIiCp5ERER+Y7ca1Am0MbZZBBGa8U5qmB2pORCTVNRFTC7OVX2gz07wrw0Wu6+rERERqRL0qVpEROQsNotBiN3CmGYRNArVL45Sc6Tluyh0FMBVN/q6lOop8RH4/Rqwh4Chj9giIiLF9K4oIiJyDoth4G+1MLhxGDfEBmvcJ6kxDuf4Y8bf5esyqhf/2pC0Bdo8q5nrRERESmGYpmn6uggREZGqqsBpklngJDklk/QCl6/LEfGqmAAro5v6YSTXAlehr8up+mJ6QrfkogHErf6+rkZERKRKUosnERGR8/CzGkQGWBnbrBYtaukXS7mypeY5cTodRbOxSdkMG7T7J3R/F/wjFTqJiIich4InERGRC7AYBn5Wg74NQhjYKAQ/9b2TK1hKjj9m/J2+LqPqCm4EN+2FphPUtU5ERKQc1NVORESkAhwuk1yHi+SUTFJznb4uR6TS1QuyMaqxtai7nanXuIeGw6HjK0Uz/1lsvq5GRESkWlCLJxERkQoonvXuzoQIOkUH+rockUr3Y44Dp8sF0d19XUrVYQ2C696ATguKZq1T6CQiIlJuCp5EREQqyDAM7BaDrnWDuKNpOEE2db2TK8vRPH/MRnf4uoyqoVZbuPkbqH9r0SDiIiIiUiEKnkRERC6Sn9WgXpCNcc1rER9q93U5IpVm16950OA2oIaHqs0ehd7bIag+2NTCUURE5GIoeBIREbkEVotBgM3CbfFh9IoNRuOOy5XgyBkHLixQ5zpfl+Ib/rXhhq3Q+m9FA4gburFFREQuloInERGRSmC3GlwTFcDYZhFEB1p9XY7IJTuRZ8dsONLXZVx+sbfAzd9C7S5F4zmJiIjIJdGsdiIiIpXINE0cJnx5Ko9tP+ZQ4NLbrFRPTcLsDK5fiLGqjq9LuTyCG0KnhRDVSYGTiIhIJVKLJxERkUpUPPB4m6gAJrSsRWKEn69LErkohzILMQ1/iOrg61K8y2KHlk9B/wMQfb1CJxERkUqm4ElERMQL7BaDQJuF/nEhjGwaToSf3nKl+vkp34rZcLivy/Ce6O4w4DtoMbloxjqLJgkQERGpbOpqJyIi4mUul4kT2J2aw39Tc3HqnVeqiRYRfgyom4Ox5ipfl1K5/OtAh5egXr+iwElERES8Rn9+FRER8TKLpaj7XYfoIMa3qEXDELWqkOrhq/QCTFsIRLTxdSmVxICmE+CWw0WDiCt0EhER8ToFTyIiIpeJn9Ug1M/K4MZh3BYfSrBNU7RL1fdrvuXK6G5Xqy30+xLaTgd7KFj9fV2RiIhIjaCudiIiIj7gdJk4Tdj2YzZ7T+ahN2OpqtpE+nNT7XSMtXG+LuXi2EKh3XSIvwusAWDo764iIiKXk955RUREfMBqMfCzGnSvF8S9zSOoG2jzdUkipfoyLR/8IyGsma9Lqbi4oTDwB4i/G2xBCp1ERER8QO++IiIiPuRntRDpb+WOhHAGNAwhzK63Zql6TuWDGTfU12WUX1Qn6LMDOi0sCs1sgb6uSEREpMZSVzsREZEqwmmauEz4Oi2fj3/OIavQ5euSRAC4trY/vSJSMd5p6utSzq/W7+B3MyCqA1gD1cJJRESkClDwJCIiUsU4XCYm8L+0PD79KYdsh96qxbcswOOtgzA2tIasFF+XU1JEa2g3A+p0BUsAWBQ4iYiIVBV6VxYREalibBYDu8WgdWQA41tG0is2mEDNgCc+5AJO57sw4273dSmewppD93egz06ISSoax0mhk4iISJWiFk8iIiJVXHELqL2/5rIjNZc8p9665fLrFB1Ij5CjGOtb+LoUCG0KbafBVX3B4gcWq68rEhERkTIoeBIREakmCp1FAdSeX3PZ9Usu+Qqg5DKyARNbB2O8mwA5x31TRHA8tH0eYgeCxQ4WzQYpIiJS1aktsoiISDVhtxr4WQ06RAfyQMtIutYNxM+iLnhyeTiAzAIHZoPBl3/nQQ2gy6vQ/wA0GFw0S51CJxERkWpBLZ5ERESqqUKnCxewMzWXPb/moknwxNu61g2kW8B3GO+1uzw7DKwHrZ+FRiOLutNZ/C7PfkVERKTSKHgSERGp5gqcLsDgy1N57Pk1l/QCJVDiHX4WeKRVCMbaRpCX6r0d1e4MzR+Dq/qBYQGrv/f2JSIiIl6lNsoiIiLVnJ+1qOd8u9oBXFM7gJ9zHOxKzeVwZgH665JUpgIXZBUUEFp/EByaW7kbtwZBozugxSQIqFv0vWaoExERqfbU4klEROQKlO904XTBZ7/msu9UHjkOvd1L5ehxVRCdbP/D2NSpcjYYlgiJD0P8KDBNsIdUznZFRESkSlDwJCIicgUrdJkYQEpmAbt+yeVEtsPXJUk1F2CBP7UKwVgTCwVpF7cRwwr1B0LzJyCi1W8z1Nkrt1ARERGpEtTVTkRE5Apm/23WuybhfjQKtZPtMNmZmsNXp/M1GLlclDwX5BQUEFx/IKQsrtiTA+pC0wmQ8GDRrHT2MO8UKSIiIlWGWjyJiIjUMAVOE8OA/6XlseeXPE7lO31dklQzvWKDuJY9GFu6l+8J0d2h+eNQ94ai7nS2QO8WKCIiIlWGgicREZEaymmauEz4NdfBZ7/mciijQK2gpFyCbQYPtgjBWB0DhRmlrxRQFxoOg2aPgl8tsAUXzVAnIiIiNYq62omIiNRQVsPAakC9YDt9A6xY4wyOnCnky7Q8DmcUoPHIpSzZDpP8wjwCYm+GH5b93wP+taHBYGhyH4S3BNNZFDiJiIhIjaUWTyIiIuIh3+nCYhh8n1nA/rR8UjILcOrTgpyjb4NgrincjrF9KNQfVBQ21WoLLodmphMRERE3BU8iIiJSpuIQKiWzgK/S8kk5o+54UtTV7pqoAH5f1w/DdIKrEOyhvi5LREREqiAFTyIiIlIu+U4XVsPgx+xCDpzO51BGAdnqj1djRPpbSQj3o2WkP7X8rbhMEz+rxmwSERGR81PwJCIiIhVW8FtLqNP5Tv6XVhRCaXa8K4sBXBVko1ktP5pH+BNgs2AANovh69JERESkGlHwJCIiIpfE4SqaHc/E5ES2g8OZBRzPKuSXXCf6kFF92Ay4KthGXLCdxuF+xATacJomNouB1VDYJCIiIhdHwZOIiIhUqkKXics0sRgGqTkODmUWcCyrkJ9zHBqkvArxtxjEhthoGGInPsyPKH8rhaaJ3TCwqlWTiIiIVBIFTyIiIuJVDpdZ1HLGMDiZ5+RwZgFHswr5MdtBgUsfQy6XIJtBgxA7jULtNAr1I8xuodBl4mc1sKhFk4iIiHiJgicRERG5rFymSYGrqGVNRoGT788U8sOZQn7KdpDl0JR5lcEAIvwtxAbbiQ+1ExdiJ9BmwWma+FkMDAVNIiIicpkoeBIRERGfcpkmha6irnkGkFHg5Nc8Jz/nODiV5+RUnpP0fCeKpEqyGRAVYCUywEptfxtXBdmICrASYrfg/G3cLX/NPCciIiI+pOBJREREqiTnb4GUAdgtBtmFLk7lOfk518HJPCcn85yk5TlrRHe9IJtBlL+VqAAb0YFWYoJs1PK3EmA1KPzt+O0WdZkTERGRqkfBk4iIiFQrLtPE4TIxAZvFoMBpkp7vJDXXyel8J7kOFzkOk1yni5zf/p9fRUc1N4BAm0GgzUKQtejfQJtBkM1CnUAbdQKsRPhZMQxwmCYWA/wsasEkIiIi1YeCJxEREbliOE0Tp8ss6pZngmGA1TCwGEWz7eU7TXIdJjkOF9mFLs4U/l84VRRUFYVal6K4hVbQbyFSoM1CiM0g1M9K8G+hUoDNwN9qwWaAwywK08zf6jUMsBlqvSQiIiJXBgVPIiIiUqMVz7pX/ImoMvIe0wQMsFAUfFktCpFERESkZlJbbRGRCkpKSmLy5Mle2/7x48dJTExk9erVXtuHiPwfm6Wo9VGArejL33rpXwE2CwFWC35Wi0InERERqdFsvi5ARC7e4cOHmTNnDl988QW//PILYWFh1K9fnw4dOjBhwgSCg4N9XWKVtmvXLnbv3s2YMWMICQnxdTk+M3nyZNasWeP+3m63U7duXXr16sUDDzxAaGioD6u7eKtXryYrK4u77rrL16WUKjEx0eN7f39/6tatyw033MB9991HZGSkV/e/ZcsWvvnmGx588EGv7kdEREREajYFTyLV1L59+xg1ahRRUVHceuut1KtXj19//ZVvv/2WZcuWMXz4cAVPF7B7925efPFFbr/99goFTxs3bsS4wsZesVgsvPDCCwBkZWXx6aefsnjxYvbt28cbb7xRLY93zZo1/Pzzz1U2eALo2LEjQ4YMASA/P5///e9/vPbaa+zYsYM1a9Z49bxv2bKFdevWKXgSEREREa9S8CRSTc2ZMwd/f3+Sk5OJioryeOzMmTP4+fld9ppcLheFhYX4+/tf9n17m2ma5OXlERgY6JNzeynKc10Mw2DgwIHu7++44w4eeOABtmzZwhdffEG7du1KfV5OTg5BQUGVXnNVVpnHHBcX53Hehw4dSnBwMIsWLeLgwYMlWkWJiIiIiFQ3GuNJpJo6cuQIjRs3LhE6AYSGhnqEDJMnTyYpKanEeqtXryYxMZHjx4+7lyUlJTF69Gg+++wzhgwZQps2bUhKSmLx4sUezy0eh2jOnDksX76cvn370rp1az755BMAFi1axMiRI+ncuTOtWrWib9++LFy4kLPnM/j73/9Oq1atyMjIKFHbnDlzSExM5OjRo+5lBw4cYPz48XTo0IE2bdpw2223sWXLllKPaefOncyYMYOuXbvSpk0bxowZw7FjxzzOyYsvvghA9+7dSUxM9DgXiYmJPPnkk2zatImBAwfSunVr3nrrLfc5OneMp8LCQubPn0///v1p3bo1nTt3ZsyYMezZs8e9TmJiIrNnzy5xrKNGjWLUqFEllp/txIkTPPvss9x00020bduW3/3ud4wePZq9e/d6rHeh61IR1113nXubxXX27t2bgwcPMmbMGNq1a8djjz0GFAVzb7zxBrfccgutW7emU6dOPPLII5w4caLEsfbu3ZuUlBTGjh1Lu3btuP7661m6dCkAP/zwA/feey/t2rWja9euzJ8/3+P5BQUFzJo1iyFDhtCxY0fatGnDwIEDS4yHlZSUxO7duzl69Kj72hbfA7t27SIxMZFdu3aVOOZzr9Hs2bNJTEzk22+/ZcqUKXTq1Inu3bu7H9+xYwd33XUX7dq1o23bttx5550e1/xi1KlTBwCbzfNvQydPnuTpp5+mW7dutGrVit69ezNv3jxcLpfHeuW590aNGsWaNWtwOp3u86OQS0RERES8QS2eRKqp2NhY9uzZw/79+2ndunWlbvvHH39kwoQJDBo0iIEDB7Jp0yZeeOEFCgsLGTdunMe6GzZs4MyZMwwbNozw8HBiY2MBWLx4MT169ODGG2/EZrOxfft2pk+fTmZmJo888ggA/fv3Z+nSpWzatInbb7/dY7vr16+nTZs2xMXFAbBnzx7Gjh1LkyZNGD9+PP7+/rz33ns88MADzJgxg5tvvtnj+dOnT8ff35/777+f06dPs2jRIh5//HGWL18OwLBhw8jKymLz5s089dRThIWFAXiMq/P555+zZcsWRowYwciRI2nUqFGp58vlcvHAAw+wbds2kpKSGDZsGA6Hg88//5zPPvuM9u3bX+SV+D/79+9n165d9OnTh3r16nH69GmSk5O5++67WbVqFQkJCR7rl3VdKuLIkSMA1KpVy70sKyuLsWPHkpSURN++fd0B59///neWL1/OgAEDGDFiBKdOnWLp0qWMGDGCt99+2+O8Zmdnc++995KUlMQNN9zAunXr+Pvf/05gYCCzZ8+mV69e9OzZk3Xr1jFjxgxatGhBt27d3Ptfvnw5/fr1Y9CgQRQWFvLBBx8wZcoUHA4HQ4cOBeDPf/4zM2bMIDMzk0mTJgFcUtfTiRMnEhMTw0MPPUR2djZQdI4nTpxIhw4d+NOf/gQUBZ+jR49m8eLFdOjQ4YLbLSgoIC0tDSjqavfVV1+xePFirrnmGho3buxeLy0tjaFDh1JQUMDQoUOJjo5m7969zJw50x1KFivPvTd+/HhcLhd79+51d7EUEREREfEKU0SqpZ07d5rNmzc3ExMTzUGDBplTp041N2/ebGZnZ5dY94knnjB79uxZYvmqVavMhIQE89ixY+5lPXv2NBMSEszk5GT3MofDYY4cOdJs06aNmZGRYZqmaR47dsxMSEgw27RpY/70008ltp2Tk1Ni2Z///Gezbdu2Zn5+vsf+7rrrLo/1vv76azMhIcFcsmSJaZqm6XK5zL59+5ojR440HQ6Hez2Xy2UOHz7cvP76602Xy+VxTMOGDfNYd/HixWZCQoJ58OBB97JZs2aZCQkJpdafkJBgJiYmmvv37y/xWM+ePc0nnnjC/f2aNWvMhIQEc9asWSXWLa6reJulrXPnnXead955p/v74nO7atUq97Lc3NwSzzt9+rTZuXNn88knnyzx3LKuS2meeOIJs3nz5uapU6fMU6dOmUePHjWXLVtmtmzZ0uzatat733feeaeZkJBgLlq0yOP5e/fuNRMSEsy33nrLY/nBgwfNli1bmjNnzvQ41oSEBHPFihXuZenp6Wbr1q3NxMREc+XKlSWWP/zww+5lDofD4/VT7O677zZ79+7tsezOO+80e/XqVWLdnTt3mgkJCebOnTtLPHbuNSp+jYwfP97jWmZnZ5sdO3Y0H330UY/n5+bmmr169TKHDRtWYtul7au0r2HDhpmnTp3yWPcvf/mL2bFjRzM1NdVj+YwZM8zExEQzJSXFvay8917xdRcRERER8SZ1tROppjp16sSbb75J7969+f7773n11Vd54IEH6NKlCwsXLrykbUdERHiMO2O1WrnzzjvJy8tjx44dHusmJSVRt27dEtsIDAwEwOFwkJGRQVpaGh07diQnJ4eUlBT3ev3792f37t38+uuv7mXr16/HYrHQr18/AL755htSUlIYMGCAe1tpaWmcPn2a7t278/PPP/P999977H/48OFYrVb39x07dgTw6G53IW3atKFVq1YXXG/jxo2EhISUaA0GVNrg0AEBAe7/5+Xlcfr0aVwuF23atOHAgQMl1i/rupTF6XTSpUsXunTpQq9evfjb3/5G48aNmT9/vse+LRYLw4cP93juhg0bCAgIoGfPnu5rk5aWRlRUFPHx8ezcudNjfbvdzqBBg9zfh4eHEx8fj81mK3X52dfMarW6x9gqLCwkPT2dtLQ0unTpwpEjRzhz5ky5j7kiRowY4XEt//vf/5Kens4tt9ziccw5OTl06dKFffv2kZube8HtXn/99SxevJjFixczd+5cHn74YQ4fPswf/vAH8vLygKJujBs3bqR79+7YbDaP/XXr1g3TND26DZb33hMRERERuRzU1U6kGrvmmmuYPXs2TqeTlJQUtm/fzoIFC5g+fTq1atXitttuu6jtNmjQoMT4MvHx8QAe40EB7q5w59q2bRsvvfQSBw4cwOFweDx2djhw8803M3/+fN577z337GMbNmygU6dO7rFuikOlZ555hmeeeabU/aWlpXl0TapXr57H48Vd6UobT6osDRo0KNd6R44coWHDhl4dVL2goICXXnqJtWvX8tNPP3k8Vr9+/RLrl3VdymKxWNyBpd1up27duqUef+3atd3BRrEffviBvLw8d3e4c527nejo6BKvr7CwMKKjoz3CwuLlqampHsvWrFnDokWLOHToUInxjc6cOUNoaOh5jvTinHs+i1+TpYWNxdLT00ucq3NFR0e7x9IC6NmzJ40bN+ahhx5i5cqVjBo1irS0NDIyMli7di1r164tdTunTp1y/7+8956IiIiIyOWg4EnkCmC1WmnatClNmzYlKSmJPn368Pbbb7uDp7Ja3Tidzkved2lhy969exk/fjxt27blmWeeISYmBrvdzoEDB/jXv/7lERYkJiaSkJDA+vXrueuuu/jiiy84fvw448ePd69j/jYo8qOPPlrmeFZNmzb1+N5iKb1Bp3nWAMsXcnZLH29yOp0lApdzTZ06lRUrVnDHHXfwu9/9jrCwMCwWC/PmzSu1FVdFQzDDMDwCkLKUdk5cLhdhYWH85z//KfU559ZS1rGWtfzsa7ZhwwYmT55Mjx49GDNmDFFRUdjtdrZt28aSJUtKBFGluZj74dzjLq5p6tSpJULOYmePa1URXbp0AeCzzz5j1KhR7mPq169fibHQihWHexW590RERERELgcFTyJXmLi4OMLDw/nll1/cy8LCwsjMzCyx7rkzjhU7duwYDofDo1VKcQuP0lrXnGvjxo3Y7XaWLFniETqU1c2tf//+/Pvf/+b48eO8++67+Pn5ceONN3ocExR1ISpPOFJeldUNrmHDhuzevZv8/PzzBj7h4eFlXocLtVDasGEDt956K0899ZTH8lmzZl1c0ZWoYcOGbN++ndatW3ultdHZ1q9fT/369Zk7d67H9Tu3O9/5FLd+O7f1T1n3Q2mKr1etWrUq9TUJuFsp5eTkAEUBVkhICIWFhRfcV0Xuvcp6/YuIiIiInI/GeBKppnbs2FFq64V9+/aRnp7u0e0sLi6OM2fO8NVXX7mXZWdn8/bbb5e67fT0dI8uPU6nk6VLl+Lv70/nzp0vWJvFYsEwDI8WJPn5+SxdurTU9fv37w/AO++8w8aNG7n++uvd4QBAy5YtadSoEYsXLy41uCmeFayiirtBlbbNiujbty/Z2dnMnz+/xGNnt9aJi4tj9+7dHo9v2bKFn3/++YL7sFgsJa73nj17+OKLLy6y6srTr18/TNMss8XTxV6f0hS3ZDv7XJw+fZpVq1aVWDcoKKjUa1u/fn2sVmuJsKqs12dpunXrRlhYGC+//DIFBQUlHr+UY966dSsAzZo1A4pagvXt25cPP/yQL7/8ssT6WVlZ7hoqcu8FBgbidDrds/SJiIiIiHiDWjyJVFNTp04lKyuLXr160aRJEwzD4LvvvmPNmjUEBAQwYcIE97o333wzM2bM4MEHH+Suu+6isLCQVatWERUVVWK8IChqwfKPf/yDb7/9lri4ODZt2sSePXt49NFHCQ8Pv2BtSUlJLFmyhDFjxnDLLbe4Q66yWgM1aNCAtm3b8sorr5Cdne0OoopZLBaef/557rnnHvr168fgwYOJjY3l5MmTfPnllxw6dIgtW7ZU8AwWBVoAM2fOpG/fvtjtdnr27ElQUFCFtnPLLbfwzjvv8OKLL/L111/TpUsXnE4nn3/+Oc2bN3d3Gxw2bBhPPfUUEyZMoHv37qSkpLB+/fpyjceUlJTE22+/TVBQEM2bN+fw4cOsXLmSpk2b+jw4aN++PaNGjeL111/n4MGDXH/99QQHB3P8+HE++OAD+vfvzx//+MdK2dcNN9zApk2buP/+++nVqxenTp1ixYoVREdHc/LkSY91W7ZsyUcffcS0adNo2bIlQUFBJCUlERISQv/+/XnjjTcwDIP4+Hh27dpVYvyy8wkJCeHZZ59l4sSJDBgwgAEDBhAdHU1qaqo7XHz99dcvuJ2jR4+6Q96CggK++eYbVqxYQWRkJKNGjXKvN3HiRHbv3s0dd9zB4MGDSUxMJCcnh0OHDrFx40beeecd6tevX6F7r/j1/9xzz9GlSxesVmuJe09ERERE5FIpeBKppiZNmsTmzZvZsWMHa9asIT8/n9q1a9OzZ0/GjRtHQkKCe93w8HDmzJnDP/7xD/71r38RHR3NmDFjCA4OZsqUKSW2Xa9ePaZOncq0adN48803qV27NpMmTeKee+4pV20dO3Zk+vTpzJs3jxdeeIGoqCgGDRpE+/btGTt2bKnPufnmm3nuuefc4cC5rr32WlauXMmcOXNYsWIFZ86cISoqimbNmvHII4+U86x56ty5M+PHj2fVqlVs27YNl8vFBx98UOHgyWKx8PLLL/PKK6+wbt06Pv74Y0JCQmjZsiUdOnRwrzd48GBOnDjBypUr2b59O23atGHBggU8//zzF9zHk08+ib+/P5s3b2b16tUkJCQwa9Ys3n333RKtqHzhqaeeolWrVrzxxhu89NJLANStW5cuXbpw0003mFnSAAAAAw5JREFUVdp+br31VtLT01m2bJl7fKWxY8cSGhpa4rU8ZswYUlJSSE5OZtGiRcTGxrpfW08++SQOh4Pk5GQsFgs9evTglVdecY+vVB433XQTMTExzJs3j9dee43c3Fzq1KlD69atGTx4cLm2sXv3bvf1s1gsREVF0bdvXx566CFiYmLc60VGRrJixQpefvllPvjgA5KTkwkNDaVhw4Y88MAD7oH4K3Lv3XLLLezbt49NmzaxZs0aTNNU8CQiIiIilc4wKzLSrohc8ZKSkoiLi2PJkiW+LkVERERERESqOY3xJCIiIiIiIiIiXqHgSUREREREREREvELBk4iIiIiIiIiIeIXGeBIREREREREREa9QiycREREREREREfEKBU8iIiIiIiIiIuIVCp5ERERERERERMQrFDyJiIiIiIiIiIhXKHgSERERERERERGvUPAkIiIiIiIiIiJeoeBJRERERERERES8QsGTiIiIiIiIiIh4hYInERERERERERHxCgVPIiIiIiIiIiLiFQqeRERERERERETEKxQ8iYiIiIiIiIiIVyh4EhERERERERERr1DwJCIiIiIiIiIiXqHgSUREREREREREvELBk4iIiIiIiIiIeIWCJxERERERERER8QoFTyIiIiIiIiIi4hUKnkRERERERERExCsUPImIiIiIiIiIiFcoeBIREREREREREa9Q8CQiIiIiIiIiIl6h4ElERERERERERLxCwZOIiIiIiIiIiHiFgicREREREREREfEKBU8iIiIiIiIiIuIVCp5ERERERERERMQrFDyJiIiIiIiIiIhXKHgSERERERERERGvUPAkIiIiIiIiIiJeoeBJRERERERERES8QsGTiIiIiIiIiIh4hYInERERERERERHxCgVPIiIiIiIiIiLiFQqeRERERERERETEKxQ8iYiIiIiIiIiIVyh4EhERERERERERr1DwJCIiIiIiIiIiXqHgSUREREREREREvELBk4iIiIiIiIiIeIWCJxERERERERER8QoFTyIiIiIiIiIi4hX/H2qZYD6bKOkJAAAAAElFTkSuQmCC\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "\"\"\"\n",
        "   Data Augmentation - MITBIH\n",
        "\n",
        "   Since our data in biased, we need to use data argumentation on it so that we can remove bias from data and make equal distributions.\n",
        "\"\"\"\n",
        "\n",
        "data_1_resample = resample(data_1, n_samples=20000,\n",
        "                           random_state=123, replace=True)\n",
        "data_2_resample = resample(data_2, n_samples=20000,\n",
        "                           random_state=123, replace=True)\n",
        "data_3_resample = resample(data_3, n_samples=20000,\n",
        "                           random_state=123, replace=True)\n",
        "data_4_resample = resample(data_4, n_samples=20000,\n",
        "                           random_state=123, replace=True)\n",
        "data_0 = data_mitbih_train[data_mitbih_train['class'] == 0].sample(n=20000, random_state=123)\n",
        "\n",
        "train_dataset_mitbih = pd.concat([data_0, data_1_resample, data_2_resample, data_3_resample,\n",
        "                                  data_4_resample])\n",
        "\n",
        "## Plot the data after Augmentation:\n",
        "\n",
        "plt.figure(figsize = (20, 10))\n",
        "my_circle = plt.Circle((0, 0), 0.7, color='white')\n",
        "plt.pie(train_dataset_mitbih['class'].value_counts(), labels=['Normal Beat', 'Unclassifiable Beats', 'Premature Ventricular Contraction',\n",
        "                                                   'Supraventricular Premature Beat', 'Fusion of Ventricular and Normal Beat'],\n",
        "        colors=['red', 'green', 'blue', 'skyblue', 'orange'], autopct = '%1.1f%%', textprops={'fontsize': 17, })\n",
        "p = plt.gcf()\n",
        "p.gca().add_artist(my_circle)\n",
        "plt.title('MITBIH Data After Augmentation Showing the Type of Heartbeats in a Pie Chart', size = 26, fontweight='bold')\n",
        "plt.savefig('MITBIH Data After Augmentation Showing the Type of Heartbeats in a Pie Chart.png')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sc1yqxo4KhVw",
        "outputId": "c6954bc1-c47d-425a-d81c-9f686609a5c5"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(100000, 187)\n",
            "(21892, 187)\n",
            "(100000, 5)\n",
            "(21892, 5)\n"
          ]
        }
      ],
      "source": [
        "\"\"\"\n",
        "   Make X & Y Variables for MITBIH Dataset and Slice it:\n",
        "\"\"\"\n",
        "target_train = train_dataset_mitbih['class']\n",
        "\n",
        "target_test = data_mitbih_test[187]\n",
        "target_train.unique()\n",
        "\n",
        "y_train = to_categorical(target_train)\n",
        "y_test = to_categorical(target_test)\n",
        "\n",
        "# Data Splicing:\n",
        "# This stage involves the data split into train & test sets. The training data will be used for training our model,\n",
        "# and the testing data will be used to check the performance of model on unseen dataset.\n",
        "\n",
        "# making train & test splits\n",
        "X_train = train_dataset_mitbih.iloc[:, :-2].values\n",
        "X_test = data_mitbih_test.iloc[:, :-1].values\n",
        "print(X_train.shape)\n",
        "print(X_test.shape)\n",
        "print(y_train.shape)\n",
        "print(y_test.shape)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "yS-X_4wScMoP"
      },
      "outputs": [],
      "source": [
        "\"\"\"\n",
        "   Compile and Build the Maachine Learning Random Forest and Support Vector Machine Models for Comparison:\n",
        "\"\"\"\n",
        "\n",
        "# Define Some Metrics:\n",
        "def recall_m(y_true, y_pred):\n",
        "    true_positives = K.sum(K.round(K.clip(y_true * y_pred, 0, 1)))\n",
        "    possible_positives = K.sum(K.round(K.clip(y_true, 0, 1)))\n",
        "    recall = true_positives / (possible_positives + K.epsilon())\n",
        "    return recall\n",
        "\n",
        "def precision_m(y_true, y_pred):\n",
        "    true_positives = K.sum(K.round(K.clip(y_true * y_pred, 0, 1)))\n",
        "    predicted_positives = K.sum(K.round(K.clip(y_pred, 0, 1)))\n",
        "    precision = true_positives / (predicted_positives + K.epsilon())\n",
        "    return precision\n",
        "\n",
        "def f1_m(y_true, y_pred):\n",
        "    precision = precision_m(y_true, y_pred)\n",
        "    recall = recall_m(y_true, y_pred)\n",
        "    return 2*((precision*recall)/(precision+recall+K.epsilon()))\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "PyY95js5dKx9",
        "outputId": "b49fd207-fc9e-461f-91e7-d945bd84dc95"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Time Taken: 104.514 seconds\n",
            "Accuracy on test data is: 0.9672\n",
            "Time Taken: 1.127 seconds\n",
            "Accuracy: 0.9671569523113466\n",
            "F1 score: 0.9743252664275995\n",
            "Recall: 0.9671569523113466\n",
            "Precision: 0.982039571762725\n",
            "\n",
            " clasification report:\n",
            "               precision    recall  f1-score   support\n",
            "\n",
            "           0       0.99      0.98      0.99     18118\n",
            "           1       0.86      0.72      0.78       556\n",
            "           2       0.97      0.91      0.94      1448\n",
            "           3       0.80      0.69      0.74       162\n",
            "           4       0.99      0.96      0.97      1608\n",
            "\n",
            "   micro avg       0.98      0.97      0.97     21892\n",
            "   macro avg       0.92      0.85      0.88     21892\n",
            "weighted avg       0.98      0.97      0.97     21892\n",
            " samples avg       0.97      0.97      0.97     21892\n",
            "\n",
            "\n",
            " confussion matrix:\n",
            " [[18004    66    29    11     8]\n",
            " [  156   398     1     0     1]\n",
            " [  106     1  1321    16     4]\n",
            " [   38     0    13   111     0]\n",
            " [   66     0     4     0  1538]]\n",
            "Normalized confusion matrix\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1080x864 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "\"\"\"\n",
        "   Model 1 - Random Forest (RF) Classifier:\n",
        "   Here, we use Random Forest Classifier to fit to our training dataset & predict results.\n",
        "\"\"\"\n",
        "# instantiate the classifier and fit to training data\n",
        "start = time.time()\n",
        "\n",
        "rf = RandomForestClassifier()\n",
        "rf.fit(X_train, y_train)\n",
        "\n",
        "end = time.time()\n",
        "print('Time Taken: %.3f seconds' % (end-start))\n",
        "\n",
        "# viewing params of random forest\n",
        "rf.get_params()\n",
        "\n",
        "all_models = list()\n",
        "performance_all = {}\n",
        "\n",
        "# Evaluate the model\n",
        "print('Accuracy on test data is: %.4f' % rf.score(X_test, y_test))\n",
        "\n",
        "# making predictions on test set\n",
        "start = time.time()\n",
        "\n",
        "y_pred_rf = rf.predict(X_test)\n",
        "\n",
        "end = time.time()\n",
        "print('Time Taken: %.3f seconds' % (end-start))\n",
        "\n",
        "print('Accuracy:', accuracy_score(y_test, y_pred_rf))\n",
        "print('F1 score:', f1_score(y_test, y_pred_rf, average='weighted'))\n",
        "print('Recall:', recall_score(y_test, y_pred_rf, average='weighted'))\n",
        "print('Precision:', precision_score(y_test, y_pred_rf, average='weighted'))\n",
        "print('\\n clasification report:\\n', classification_report(y_test, y_pred_rf))\n",
        "print('\\n confussion matrix:\\n', confusion_matrix(y_test.argmax(axis=1), y_pred_rf.argmax(axis=1)))\n",
        "\n",
        "performance_all['RF_Model'] = accuracy_score(y_test, y_pred_rf), f1_score(y_test, y_pred_rf, average='weighted'), recall_score(y_test, y_pred_rf, average='weighted'), precision_score(y_test, y_pred_rf, average='weighted')\n",
        "\n",
        "model1 = rf\n",
        "all_models.append(model1)\n",
        "\n",
        "# Plot Model's Confusion Matrix:\n",
        "\n",
        "def plot_confusion_matrix(cm, classes,\n",
        "                          normalize=False,\n",
        "                          title='Confusion matrix',\n",
        "                          cmap=plt.cm.Blues):\n",
        "    if normalize:\n",
        "        cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\n",
        "        print(\"Normalized confusion matrix\")\n",
        "    else:\n",
        "        print('Confusion matrix, without normalization')\n",
        "\n",
        "    plt.imshow(cm, interpolation='nearest', cmap=cmap)\n",
        "    plt.title(title, size = 20, fontweight='bold')\n",
        "    plt.colorbar()\n",
        "    tick_marks = np.arange(len(classes))\n",
        "    plt.xticks(tick_marks, classes, rotation=45, fontsize=18)\n",
        "    plt.yticks(tick_marks, classes, fontsize=18)\n",
        "\n",
        "    fmt = '.2f' if normalize else 'd'\n",
        "    thresh = cm.max() / 2.\n",
        "    for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\n",
        "        plt.text(j, i, format(cm[i, j], fmt),\n",
        "                 horizontalalignment=\"center\",\n",
        "                 color=\"white\" if cm[i, j] > thresh else \"black\")\n",
        "\n",
        "    plt.tight_layout()\n",
        "    plt.ylabel('True Label', weight='bold').set_fontsize('18')\n",
        "    plt.xlabel('Predicted Label', weight='bold').set_fontsize('18')\n",
        "    plt.savefig('Confusion Matrix for Random Forest Model.png')\n",
        "\n",
        "cf_matrix = confusion_matrix(y_test.argmax(axis=1), y_pred_rf.argmax(axis=1))\n",
        "plt.figure(figsize=(15, 12))\n",
        "plot_confusion_matrix(cf_matrix, classes=['N', 'S', 'V', 'F', 'Q'],\n",
        "                      normalize = True, title='Confusion Matrix With Normalization - Random Forest Model')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "Wt10VEovdj8D",
        "outputId": "963ec092-3018-4c35-d346-fdc9ca9a060f"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy on test data is: 0.8423\n",
            "Time Taken: 255.001 seconds\n",
            "Accuracy: 0.8423168280650466\n",
            "F1 score: 0.8985948028308774\n",
            "Recall: 0.8524575187283026\n",
            "Precision: 0.9624733658194119\n",
            "\n",
            " clasification report:\n",
            "               precision    recall  f1-score   support\n",
            "\n",
            "           0       0.99      0.84      0.91     18118\n",
            "           1       0.38      0.79      0.52       556\n",
            "           2       0.90      0.89      0.89      1448\n",
            "           3       0.17      0.91      0.29       162\n",
            "           4       0.95      0.97      0.96      1608\n",
            "\n",
            "   micro avg       0.92      0.85      0.88     21892\n",
            "   macro avg       0.68      0.88      0.71     21892\n",
            "weighted avg       0.96      0.85      0.90     21892\n",
            " samples avg       0.85      0.85      0.85     21892\n",
            "\n",
            "\n",
            " confussion matrix:\n",
            " [[16799   581    95   602    41]\n",
            " [  110   434     4     6     2]\n",
            " [  109     9  1274    53     3]\n",
            " [   11     0     4   147     0]\n",
            " [   40     5     9     1  1553]]\n",
            "Normalized confusion matrix\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1080x864 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "\"\"\"\n",
        "   Model 2 - Support Vector Machine (SVM):\n",
        "   Similar to RF Model, we'll fit SVM on our training dataset and check its performance on test data.\n",
        "\"\"\"\n",
        "# instantiate and fit SVM to train data\n",
        "\n",
        "svc = SVC()\n",
        "clf = OneVsRestClassifier(estimator=svc)\n",
        "clf.fit(X_train, y_train)\n",
        "\n",
        "# check parameters of SVM\n",
        "clf.get_params()\n",
        "\n",
        "# Evaluate the model\n",
        "print('Accuracy on test data is: %.4f' % clf.score(X_test, y_test))\n",
        "\n",
        "# making predictions on test set\n",
        "start = time.time()\n",
        "\n",
        "y_pred_svc = clf.predict(X_test)\n",
        "\n",
        "end = time.time()\n",
        "print('Time Taken: %.3f seconds' % (end-start))\n",
        "\n",
        "print('Accuracy:', accuracy_score(y_test, y_pred_svc))\n",
        "print('F1 score:', f1_score(y_test, y_pred_svc, average='weighted'))\n",
        "print('Recall:', recall_score(y_test, y_pred_svc, average='weighted'))\n",
        "print('Precision:', precision_score(y_test, y_pred_svc, average='weighted'))\n",
        "print('\\n clasification report:\\n', classification_report(y_test, y_pred_svc))\n",
        "print('\\n confussion matrix:\\n', confusion_matrix(y_test.argmax(axis=1), y_pred_svc.argmax(axis=1)))\n",
        "\n",
        "performance_all['SVC_Model'] = accuracy_score(y_test, y_pred_svc), f1_score(y_test, y_pred_svc, average='weighted'), recall_score(y_test, y_pred_svc, average='weighted'), precision_score(y_test, y_pred_svc, average='weighted')\n",
        "\n",
        "model2 = clf\n",
        "all_models.append(model2)\n",
        "\n",
        "# Plot Model's Confusion Matrix:\n",
        "\n",
        "def plot_confusion_matrix(cm, classes,\n",
        "                          normalize=False,\n",
        "                          title='Confusion matrix',\n",
        "                          cmap=plt.cm.Blues):\n",
        "    if normalize:\n",
        "        cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\n",
        "        print(\"Normalized confusion matrix\")\n",
        "    else:\n",
        "        print('Confusion matrix, without normalization')\n",
        "\n",
        "    plt.imshow(cm, interpolation='nearest', cmap=cmap)\n",
        "    plt.title(title, size = 20, fontweight='bold')\n",
        "    plt.colorbar()\n",
        "    tick_marks = np.arange(len(classes))\n",
        "    plt.xticks(tick_marks, classes, rotation=45, fontsize=18)\n",
        "    plt.yticks(tick_marks, classes, fontsize=18)\n",
        "\n",
        "    fmt = '.2f' if normalize else 'd'\n",
        "    thresh = cm.max() / 2.\n",
        "    for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\n",
        "        plt.text(j, i, format(cm[i, j], fmt),\n",
        "                 horizontalalignment=\"center\",\n",
        "                 color=\"white\" if cm[i, j] > thresh else \"black\")\n",
        "\n",
        "    plt.tight_layout()\n",
        "    plt.ylabel('True Label', weight='bold').set_fontsize('18')\n",
        "    plt.xlabel('Predicted Label', weight='bold').set_fontsize('18')\n",
        "    plt.savefig('Confusion Matrix for Support Vector Machine Model.png')\n",
        "\n",
        "cf_matrix = confusion_matrix(y_test.argmax(axis=1), y_pred_svc.argmax(axis=1))\n",
        "plt.figure(figsize=(15, 12))\n",
        "plot_confusion_matrix(cf_matrix, classes=['N', 'S', 'V', 'F', 'Q'],\n",
        "                      normalize = True, title='Confusion Matrix With Normalization - Support Vector Machine Model')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RWa6TE0DIcIP",
        "outputId": "2d728271-ece1-4cbe-d2d3-d7a5b6347daa"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "The hyperparameter search is complete. The optimal learning rate for the optimizer\n",
            "is 0.001, the optimal Optimizer is sgd,\n",
            "the optimal batch size for the optimizer is 64, the optimal epoch for the optimizer\n",
            "is 100, and the Best Hyperparameters values are {'optimizer': 'sgd', 'batch_size': 64, 'epochs': 100, 'learning_rate': 0.001}.\n",
            "\n"
          ]
        }
      ],
      "source": [
        "\"\"\"\n",
        "   Compile, Build, and Perform Hyperparameters Optimization in order to Build the Deep Learning CNN, CNN_BiLSTM, and\n",
        "   Attention Based CNN_BiLSTM Models:\n",
        "\"\"\"\n",
        "\n",
        "\"\"\"\n",
        "   Model 3 - Convolutional Neural Network (CNN)\n",
        "\n",
        "   We will apply the CNN algorithm to our data to generate prediction results. First, we need to reshape our data for CNN.\n",
        "   We will use 1-dimensional CNN for our model, reshaping our data as per the dimensions of our CNN\n",
        "\"\"\"\n",
        "\n",
        "\"\"\"Note that the timeseries data used here are univariate, meaning we only have one channel per timeseries example.\n",
        "We will therefore transform the timeseries into a multivariate one with one channel using a simple\n",
        "reshaping via numpy. This will allow us to construct a model that is easily applicable to multivariate time series\"\"\"\n",
        "\n",
        "\n",
        "# Reshape the Data:\n",
        "X_train = X_train.reshape(len(X_train), X_train.shape[1], 1)\n",
        "X_test = X_test.reshape(len(X_test), X_test.shape[1], 1)\n",
        "X_train.shape, X_test.shape\n",
        "\n",
        "# Compile the CNN Model:\n",
        "def call_existing_code(hp, optimizer, batch_size, epochs):\n",
        "    model = Sequential()\n",
        "    model.add(Conv1D(filters=128, kernel_size=6, activation='relu',\n",
        "                    padding='same', input_shape=(187, 1)))\n",
        "    model.add(BatchNormalization())\n",
        "\n",
        "    # adding a pooling layer\n",
        "    model.add(MaxPooling1D(pool_size=(3), strides=2, padding='same'))\n",
        "\n",
        "    model.add(Conv1D(filters=64, kernel_size=6, activation='relu',\n",
        "                    padding='same', input_shape=(187, 1)))\n",
        "    model.add(BatchNormalization())\n",
        "    model.add(MaxPooling1D(pool_size=(3), strides=2, padding='same'))\n",
        "\n",
        "    model.add(Conv1D(filters=64, kernel_size=6, activation='relu',\n",
        "                    padding='same', input_shape=(187, 1)))\n",
        "    model.add(BatchNormalization())\n",
        "    model.add(MaxPooling1D(pool_size=(3), strides=2, padding='same'))\n",
        "\n",
        "    model.add(Flatten())\n",
        "    model.add(Dropout(0.2))\n",
        "    model.add(Dense(64, activation='relu'))\n",
        "    model.add(Dropout(0.2))\n",
        "    model.add(Dense(64, activation='relu'))\n",
        "    model.add(Dropout(0.2))\n",
        "    model.add(Dense(5, activation='softmax'))\n",
        "\n",
        "    hp_learning_rate = hp.Choice('learning_rate', values=[1e-2, 1e-3, 1e-4])\n",
        "    model.compile(loss='categorical_crossentropy',\n",
        "                  optimizer = keras.optimizers.Adam(learning_rate=hp_learning_rate),\n",
        "                  metrics=['accuracy', f1_m, precision_m, recall_m])\n",
        "    return model\n",
        "\n",
        "def build_model(hp):\n",
        "    optimizer = hp.Choice(\"optimizer\", [\"adam\", \"sgd\"]),\n",
        "    batch_size = hp.Int('batch_size', 32, 256, step=32),\n",
        "    epochs = hp.Int('epochs', 10, 100, step=10),\n",
        "\n",
        "    # call existing model-building code with the hyperparameter values.\n",
        "    model = call_existing_code(hp, optimizer=optimizer, batch_size=batch_size,\n",
        "                               epochs=epochs)\n",
        "    return model\n",
        "\n",
        "build_model(keras_tuner.HyperParameters())\n",
        "\n",
        "tuner = keras_tuner.RandomSearch(\n",
        "    hypermodel = build_model,\n",
        "    objective=\"val_accuracy\",\n",
        "    max_trials=2,\n",
        "    executions_per_trial=1,\n",
        "    overwrite=True,\n",
        "    directory=\"my_dir\",\n",
        "    project_name=\"CNN_MITBIH\",\n",
        ")\n",
        "\n",
        "# print a summary of the search space:\n",
        "tuner.search_space_summary()\n",
        "\n",
        "tuner.search(\n",
        "    X_train, y_train, validation_split=0.3, epochs = 10,\n",
        ")\n",
        "\n",
        "print(tuner.results_summary())\n",
        "\n",
        "# Get the optimal hyperparameters\n",
        "best_hp = tuner.get_best_hyperparameters()[0]\n",
        "\n",
        "# Get the best model:\n",
        "new_model = tuner.get_best_models()[0]\n",
        "\n",
        "IPython.display.clear_output()\n",
        "\n",
        "print(f\"\"\"\n",
        "The hyperparameter search is complete. The optimal learning rate for the optimizer\n",
        "is {best_hp.get('learning_rate')}, the optimal Optimizer is {best_hp.get('optimizer')},\n",
        "the optimal batch size for the optimizer is {best_hp.get('batch_size')}, the optimal epoch for the optimizer\n",
        "is {best_hp.get('epochs')}, and the Best Hyperparameters values are {best_hp.values}.\n",
        "\"\"\")"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\"\"\"\n",
        "    Retrain the model:\n",
        "\"\"\"\n",
        "# Build the model with the best hp.\n",
        "callbacks = [\n",
        "    keras.callbacks.ModelCheckpoint(\n",
        "        \"best_model_CNN.h5\", save_best_only=True, monitor=\"val_loss\"\n",
        "    ),\n",
        "    keras.callbacks.EarlyStopping(monitor=\"val_loss\", patience=50, verbose=1),\n",
        "    ]\n",
        "\n",
        "model = new_model\n",
        "\n",
        "# Fit with the entire dataset.\n",
        "model.fit(X_train, y_train, validation_split=0.3, epochs=5,\n",
        "          callbacks=callbacks,\n",
        "          verbose=1,\n",
        "          )\n",
        "\n",
        "performance = {}\n",
        "\n",
        "# Evaluate the model\n",
        "performance['CNN_MITBIH'] = model.evaluate(X_test, y_test, verbose = 0)\n",
        "print(\"[loss:, accuracy :, f1_m :,  precision_m :, recall_m :]\", performance['CNN_MITBIH'])\n",
        "\n",
        "# Evaluate the model\n",
        "loss, accuracy, f1_score, precision, recall = model.evaluate(X_test, y_test, verbose=0)\n",
        "print(\"Test accuracy\", accuracy)\n",
        "print(\"Test F1-Score\", f1_score)\n",
        "print(\"Test Precision\", precision)\n",
        "print(\"Test Recall\", recall)\n",
        "\n",
        "# making predictions on test set\n",
        "start = time.time()\n",
        "y_pred_CNN = model.predict(X_test)\n",
        "end = time.time()\n",
        "print('Time Taken: %.3f seconds' % (end-start))\n",
        "\n",
        "#performance_all['CNN_Model'] = accuracy, f1_score, recall_score(), precision_score()\n",
        "\n",
        "model3 = model\n",
        "all_models.append(model3)\n",
        "\n",
        "model3.summary()\n",
        "\n",
        "keras.utils.plot_model(model3, show_shapes=True)\n",
        "\n",
        "# Plot Model's Confusion Matrix:\n",
        "\n",
        "def plot_confusion_matrix(cm, classes,\n",
        "                          normalize=False,\n",
        "                          title='Confusion matrix',\n",
        "                          cmap=plt.cm.Blues):\n",
        "    if normalize:\n",
        "        cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\n",
        "        print(\"Normalized confusion matrix\")\n",
        "    else:\n",
        "        print('Confusion matrix, without normalization')\n",
        "\n",
        "    plt.imshow(cm, interpolation='nearest', cmap=cmap)\n",
        "    plt.title(title, size = 26, fontweight='bold')\n",
        "    plt.colorbar()\n",
        "    tick_marks = np.arange(len(classes))\n",
        "    plt.xticks(tick_marks, classes, rotation=45, fontsize=18)\n",
        "    plt.yticks(tick_marks, classes, fontsize=18)\n",
        "\n",
        "    fmt = '.2f' if normalize else 'd'\n",
        "    thresh = cm.max() / 2.\n",
        "    for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\n",
        "        plt.text(j, i, format(cm[i, j], fmt),\n",
        "                 horizontalalignment=\"center\",\n",
        "                 color=\"white\" if cm[i, j] > thresh else \"black\")\n",
        "\n",
        "    plt.tight_layout()\n",
        "    plt.ylabel('True Label', weight='bold').set_fontsize('18')\n",
        "    plt.xlabel('Predicted Label', weight='bold').set_fontsize('18')\n",
        "    plt.savefig('Confusion Matrix for CNN Model.png')\n",
        "\n",
        "cf_matrix = confusion_matrix(y_test.argmax(axis=1), y_pred_CNN.argmax(axis=1))\n",
        "plt.figure(figsize=(15, 12))\n",
        "plot_confusion_matrix(cf_matrix, classes=['N', 'S', 'V', 'F', 'Q'],\n",
        "                      normalize = True, title='Confusion Matrix With Normalization - CNN Model')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "qa83Voaiiooo",
        "outputId": "a67efd98-8e1d-4f14-b50d-c9b2525cee2c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/5\n",
            "2188/2188 [==============================] - 144s 66ms/step - loss: 0.0295 - accuracy: 0.9907 - f1_m: 0.9907 - precision_m: 0.9911 - recall_m: 0.9903 - val_loss: 131.3570 - val_accuracy: 0.3333 - val_f1_m: 0.3332 - val_precision_m: 0.3332 - val_recall_m: 0.3332\n",
            "Epoch 2/5\n",
            "2188/2188 [==============================] - 140s 64ms/step - loss: 0.0294 - accuracy: 0.9910 - f1_m: 0.9909 - precision_m: 0.9913 - recall_m: 0.9905 - val_loss: 144.0146 - val_accuracy: 0.3333 - val_f1_m: 0.3332 - val_precision_m: 0.3332 - val_recall_m: 0.3332\n",
            "Epoch 3/5\n",
            "2188/2188 [==============================] - 135s 62ms/step - loss: 0.0285 - accuracy: 0.9913 - f1_m: 0.9912 - precision_m: 0.9916 - recall_m: 0.9909 - val_loss: 129.7681 - val_accuracy: 0.3333 - val_f1_m: 0.3332 - val_precision_m: 0.3332 - val_recall_m: 0.3332\n",
            "Epoch 4/5\n",
            "2188/2188 [==============================] - 133s 61ms/step - loss: 0.0265 - accuracy: 0.9916 - f1_m: 0.9916 - precision_m: 0.9920 - recall_m: 0.9912 - val_loss: 174.4849 - val_accuracy: 0.3333 - val_f1_m: 0.3332 - val_precision_m: 0.3332 - val_recall_m: 0.3332\n",
            "Epoch 5/5\n",
            "2188/2188 [==============================] - 133s 61ms/step - loss: 0.0260 - accuracy: 0.9922 - f1_m: 0.9922 - precision_m: 0.9926 - recall_m: 0.9919 - val_loss: 164.2305 - val_accuracy: 0.3310 - val_f1_m: 0.3309 - val_precision_m: 0.3309 - val_recall_m: 0.3309\n",
            "[loss:, accuracy :, f1_m :,  precision_m :, recall_m :] [17.959861755371094, 0.9115201830863953, 0.9103654026985168, 0.9106104969978333, 0.9101277589797974]\n",
            "Test accuracy 0.9115201830863953\n",
            "Test F1-Score 0.9103654026985168\n",
            "Test Precision 0.9106104969978333\n",
            "Test Recall 0.9101277589797974\n",
            "Time Taken: 8.021 seconds\n",
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " conv1d (Conv1D)             (None, 187, 128)          896       \n",
            "                                                                 \n",
            " batch_normalization (BatchN  (None, 187, 128)         512       \n",
            " ormalization)                                                   \n",
            "                                                                 \n",
            " max_pooling1d (MaxPooling1D  (None, 94, 128)          0         \n",
            " )                                                               \n",
            "                                                                 \n",
            " conv1d_1 (Conv1D)           (None, 94, 64)            49216     \n",
            "                                                                 \n",
            " batch_normalization_1 (Batc  (None, 94, 64)           256       \n",
            " hNormalization)                                                 \n",
            "                                                                 \n",
            " max_pooling1d_1 (MaxPooling  (None, 47, 64)           0         \n",
            " 1D)                                                             \n",
            "                                                                 \n",
            " conv1d_2 (Conv1D)           (None, 47, 64)            24640     \n",
            "                                                                 \n",
            " batch_normalization_2 (Batc  (None, 47, 64)           256       \n",
            " hNormalization)                                                 \n",
            "                                                                 \n",
            " max_pooling1d_2 (MaxPooling  (None, 24, 64)           0         \n",
            " 1D)                                                             \n",
            "                                                                 \n",
            " flatten (Flatten)           (None, 1536)              0         \n",
            "                                                                 \n",
            " dropout (Dropout)           (None, 1536)              0         \n",
            "                                                                 \n",
            " dense (Dense)               (None, 64)                98368     \n",
            "                                                                 \n",
            " dropout_1 (Dropout)         (None, 64)                0         \n",
            "                                                                 \n",
            " dense_1 (Dense)             (None, 64)                4160      \n",
            "                                                                 \n",
            " dropout_2 (Dropout)         (None, 64)                0         \n",
            "                                                                 \n",
            " dense_2 (Dense)             (None, 5)                 325       \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 178,629\n",
            "Trainable params: 178,117\n",
            "Non-trainable params: 512\n",
            "_________________________________________________________________\n",
            "Normalized confusion matrix\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1080x864 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "tRCP60zsx4jd",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "2f736ce1-626b-42a0-ed76-4327c93dd3f9"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "The hyperparameter search is complete. The optimal learning rate for the optimizer\n",
            "is 0.01, the optimal Optimizer is sgd,\n",
            "the optimal batch size for the optimizer is 96, the optimal epoch for the optimizer\n",
            "is 20, and the Best Hyperparameters values are {'optimizer': 'sgd', 'batch_size': 96, 'epochs': 20, 'learning_rate': 0.01}.\n",
            "\n"
          ]
        }
      ],
      "source": [
        "\"\"\"\n",
        "   Model 4 - Hybrid CNN and BiLSTM (CNN_BiLSTM):\n",
        "\n",
        "   Convolutional neural networks excel at learning the spatial structure in input data.\n",
        "   The data does have a one-dimensional spatial structure in the sequence of words in reviews, and the CNN may be able to\n",
        "   pick out invariant features for the good and bad sentiment. This learned spatial feature may then be\n",
        "   learned as sequences by an BiLSTM layer.\n",
        "   You can easily add a one-dimensional CNN and max pooling layers after the Embedding layer, which then\n",
        "   feeds the consolidated features to the BiLSTM. You can use a smallish set of 32 features with a small filter\n",
        "   length of 3. The pooling layer can use the standard length of 2 to halve the feature map size.\n",
        "\"\"\"\n",
        "# Compile the Model:\n",
        "def call_existing_code(hp, optimizer, batch_size, epochs):\n",
        "\n",
        "    model = Sequential()\n",
        "\n",
        "    model.add(Conv1D(filters=128, kernel_size=6, activation='relu',\n",
        "                    padding='same', input_shape=(187, 1)))\n",
        "    model.add(BatchNormalization())\n",
        "\n",
        "    # adding a pooling layer\n",
        "    model.add(MaxPooling1D(pool_size=(3), strides=2, padding='same'))\n",
        "\n",
        "    model.add(Conv1D(filters=64, kernel_size=6, activation='relu',\n",
        "                    padding='same', input_shape=(187, 1)))\n",
        "    model.add(BatchNormalization())\n",
        "    model.add(MaxPooling1D(pool_size=(3), strides=2, padding='same'))\n",
        "\n",
        "    model.add(Conv1D(filters=64, kernel_size=6, activation='relu',\n",
        "                    padding='same', input_shape=(187, 1)))\n",
        "    model.add(BatchNormalization())\n",
        "    model.add(MaxPooling1D(pool_size=(3), strides=2, padding='same'))\n",
        "\n",
        "    model.add(Bidirectional(LSTM(256, dropout=0.2, recurrent_dropout=0.2)))\n",
        "    model.add(Dropout(0.2))\n",
        "    model.add(Dense(64, activation='relu'))\n",
        "    model.add(Dropout(0.2))\n",
        "    model.add(Dense(5, activation='softmax'))\n",
        "\n",
        "    hp_learning_rate = hp.Choice('learning_rate', values=[1e-2, 1e-3, 1e-4])\n",
        "    model.compile(loss='categorical_crossentropy',\n",
        "                  optimizer = keras.optimizers.Adam(learning_rate=hp_learning_rate),\n",
        "                  metrics=['accuracy', f1_m, precision_m, recall_m])\n",
        "    return model\n",
        "\n",
        "def build_model(hp):\n",
        "    optimizer = hp.Choice(\"optimizer\", [\"adam\", \"sgd\"]),\n",
        "    batch_size = hp.Int('batch_size', 32, 256, step=32),\n",
        "    epochs = hp.Int('epochs', 10, 100, step=10),\n",
        "\n",
        "    # call existing model-building code with the hyperparameter values.\n",
        "    model = call_existing_code(hp, optimizer=optimizer, batch_size=batch_size,\n",
        "                               epochs=epochs)\n",
        "    return model\n",
        "\n",
        "build_model(keras_tuner.HyperParameters())\n",
        "\n",
        "tuner = keras_tuner.RandomSearch(\n",
        "    hypermodel = build_model,\n",
        "    objective=\"val_accuracy\",\n",
        "    max_trials=2,\n",
        "    executions_per_trial=1,\n",
        "    overwrite=True,\n",
        "    directory=\"my_dir\",\n",
        "    project_name=\"CNN_BiLSTM_MITBIH\",\n",
        ")\n",
        "\n",
        "# print a summary of the search space:\n",
        "tuner.search_space_summary()\n",
        "\n",
        "tuner.search(\n",
        "    X_train, y_train, validation_split=0.3, epochs = 10,\n",
        ")\n",
        "\n",
        "print(tuner.results_summary())\n",
        "\n",
        "# Get the optimal hyperparameters\n",
        "best_hp = tuner.get_best_hyperparameters()[0]\n",
        "\n",
        "# Get the best model:\n",
        "new_model = tuner.get_best_models()[0]\n",
        "\n",
        "IPython.display.clear_output()\n",
        "\n",
        "print(f\"\"\"\n",
        "The hyperparameter search is complete. The optimal learning rate for the optimizer\n",
        "is {best_hp.get('learning_rate')}, the optimal Optimizer is {best_hp.get('optimizer')},\n",
        "the optimal batch size for the optimizer is {best_hp.get('batch_size')}, the optimal epoch for the optimizer\n",
        "is {best_hp.get('epochs')}, and the Best Hyperparameters values are {best_hp.values}.\n",
        "\"\"\")\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\"\"\"\n",
        "    Retrain the model:\n",
        "\"\"\"\n",
        "# Build the model with the best hp.\n",
        "callbacks = [\n",
        "    keras.callbacks.ModelCheckpoint(\n",
        "        \"best_model_CNN_BiLSTM.h5\", save_best_only=True, monitor=\"val_loss\"\n",
        "    ),\n",
        "    keras.callbacks.EarlyStopping(monitor=\"val_loss\", patience=50, verbose=1),\n",
        "    ]\n",
        "\n",
        "model = new_model\n",
        "\n",
        "# Fit with the entire dataset.\n",
        "model.fit(X_train, y_train, validation_split=0.3, epochs = 5,\n",
        "          callbacks=callbacks,\n",
        "          verbose=1,\n",
        "          )\n",
        "\n",
        "# Evaluate the model\n",
        "performance['CNN_BiLSTM_MITBIH'] = model.evaluate(X_test, y_test, verbose = 0)\n",
        "print(\"[loss:, accuracy :, f1_m :,  precision_m :, recall_m :]\", performance['CNN_BiLSTM_MITBIH'])\n",
        "\n",
        "# Evaluate the model\n",
        "loss, accuracy, f1_score, precision, recall = model.evaluate(X_test, y_test, verbose=0)\n",
        "print(\"Test accuracy\", accuracy)\n",
        "print(\"Test F1-Score\", f1_score)\n",
        "print(\"Test Precision\", precision)\n",
        "print(\"Test Recall\", recall)\n",
        "\n",
        "# making predictions on test set\n",
        "start = time.time()\n",
        "y_pred_CNN_LSTM = model.predict(X_test)\n",
        "end = time.time()\n",
        "print('Time Taken: %.3f seconds' % (end-start))\n",
        "\n",
        "model4 = model\n",
        "all_models.append(model4)\n",
        "\n",
        "model4.summary()\n",
        "\n",
        "keras.utils.plot_model(model4, show_shapes=True)\n",
        "\n",
        "# Plot Model's Confusion Matrix:\n",
        "\n",
        "def plot_confusion_matrix(cm, classes,\n",
        "                          normalize=False,\n",
        "                          title='Confusion matrix',\n",
        "                          cmap=plt.cm.Blues):\n",
        "    if normalize:\n",
        "        cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\n",
        "        print(\"Normalized confusion matrix\")\n",
        "    else:\n",
        "        print('Confusion matrix, without normalization')\n",
        "\n",
        "    plt.imshow(cm, interpolation='nearest', cmap=cmap)\n",
        "    plt.title(title, size = 20, fontweight='bold')\n",
        "    plt.colorbar()\n",
        "    tick_marks = np.arange(len(classes))\n",
        "    plt.xticks(tick_marks, classes, rotation=45, fontsize=18)\n",
        "    plt.yticks(tick_marks, classes, fontsize=18)\n",
        "\n",
        "    fmt = '.2f' if normalize else 'd'\n",
        "    thresh = cm.max() / 2.\n",
        "    for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\n",
        "        plt.text(j, i, format(cm[i, j], fmt),\n",
        "                 horizontalalignment=\"center\",\n",
        "                 color=\"white\" if cm[i, j] > thresh else \"black\")\n",
        "\n",
        "    plt.tight_layout()\n",
        "    plt.ylabel('True Label', weight='bold').set_fontsize('18')\n",
        "    plt.xlabel('Predicted Label', weight='bold').set_fontsize('18')\n",
        "    plt.savefig('Confusion Matrix for CNN_BiLSTM Model.png')\n",
        "\n",
        "cf_matrix = confusion_matrix(y_test.argmax(axis=1), y_pred_CNN_LSTM.argmax(axis=1))\n",
        "plt.figure(figsize=(15, 12))\n",
        "plot_confusion_matrix(cf_matrix, classes=['N', 'S', 'V', 'F', 'Q'],\n",
        "                      normalize = True, title='Confusion Matrix With Normalization - CNN_BiLSTM Model')\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "DDXE4JPinCvI",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "94c726a7-eef4-4ad3-e89b-806b4e73102c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/5\n",
            "2188/2188 [==============================] - 855s 388ms/step - loss: 0.6503 - accuracy: 0.7502 - f1_m: 0.7297 - precision_m: 0.8065 - recall_m: 0.6690 - val_loss: 18.4720 - val_accuracy: 0.2703 - val_f1_m: 0.2758 - val_precision_m: 0.2839 - val_recall_m: 0.2684\n",
            "Epoch 2/5\n",
            "2188/2188 [==============================] - 850s 388ms/step - loss: 0.6145 - accuracy: 0.7681 - f1_m: 0.7512 - precision_m: 0.8224 - recall_m: 0.6935 - val_loss: 21.7947 - val_accuracy: 0.1234 - val_f1_m: 0.1127 - val_precision_m: 0.1415 - val_recall_m: 0.0943\n",
            "Epoch 3/5\n",
            "2188/2188 [==============================] - 845s 386ms/step - loss: 0.6132 - accuracy: 0.7658 - f1_m: 0.7522 - precision_m: 0.8089 - recall_m: 0.7058 - val_loss: 30.7911 - val_accuracy: 0.2869 - val_f1_m: 0.2946 - val_precision_m: 0.3122 - val_recall_m: 0.2792\n",
            "Epoch 4/5\n",
            "2188/2188 [==============================] - 844s 386ms/step - loss: 0.5985 - accuracy: 0.7702 - f1_m: 0.7599 - precision_m: 0.8132 - recall_m: 0.7153 - val_loss: 41.8562 - val_accuracy: 0.2878 - val_f1_m: 0.2972 - val_precision_m: 0.3096 - val_recall_m: 0.2862\n",
            "Epoch 5/5\n",
            "2188/2188 [==============================] - 842s 385ms/step - loss: 0.5555 - accuracy: 0.7898 - f1_m: 0.7809 - precision_m: 0.8301 - recall_m: 0.7394 - val_loss: 38.1605 - val_accuracy: 0.2768 - val_f1_m: 0.2805 - val_precision_m: 0.2876 - val_recall_m: 0.2739\n",
            "[loss:, accuracy :, f1_m :,  precision_m :, recall_m :] [4.717374801635742, 0.808012068271637, 0.7965505123138428, 0.8203678131103516, 0.7747718691825867]\n",
            "Test accuracy 0.808012068271637\n",
            "Test F1-Score 0.7965505123138428\n",
            "Test Precision 0.8203678131103516\n",
            "Test Recall 0.7747718691825867\n",
            "Time Taken: 32.001 seconds\n",
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " conv1d (Conv1D)             (None, 187, 128)          896       \n",
            "                                                                 \n",
            " batch_normalization (BatchN  (None, 187, 128)         512       \n",
            " ormalization)                                                   \n",
            "                                                                 \n",
            " max_pooling1d (MaxPooling1D  (None, 94, 128)          0         \n",
            " )                                                               \n",
            "                                                                 \n",
            " conv1d_1 (Conv1D)           (None, 94, 64)            49216     \n",
            "                                                                 \n",
            " batch_normalization_1 (Batc  (None, 94, 64)           256       \n",
            " hNormalization)                                                 \n",
            "                                                                 \n",
            " max_pooling1d_1 (MaxPooling  (None, 47, 64)           0         \n",
            " 1D)                                                             \n",
            "                                                                 \n",
            " conv1d_2 (Conv1D)           (None, 47, 64)            24640     \n",
            "                                                                 \n",
            " batch_normalization_2 (Batc  (None, 47, 64)           256       \n",
            " hNormalization)                                                 \n",
            "                                                                 \n",
            " max_pooling1d_2 (MaxPooling  (None, 24, 64)           0         \n",
            " 1D)                                                             \n",
            "                                                                 \n",
            " bidirectional (Bidirectiona  (None, 512)              657408    \n",
            " l)                                                              \n",
            "                                                                 \n",
            " dropout (Dropout)           (None, 512)               0         \n",
            "                                                                 \n",
            " dense (Dense)               (None, 64)                32832     \n",
            "                                                                 \n",
            " dropout_1 (Dropout)         (None, 64)                0         \n",
            "                                                                 \n",
            " dense_1 (Dense)             (None, 5)                 325       \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 766,341\n",
            "Trainable params: 765,829\n",
            "Non-trainable params: 512\n",
            "_________________________________________________________________\n",
            "Normalized confusion matrix\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1080x864 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "3LU22jnsL6cG",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "38603377-3af9-4e44-f180-17ac80f71cbf"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "The hyperparameter search is complete. The optimal learning rate for the optimizer\n",
            "is 0.0001, the optimal Optimizer is adam,\n",
            "the optimal batch size for the optimizer is 128, the optimal epoch for the optimizer\n",
            "is 20, and the Best Hyperparameters values are {'optimizer': 'adam', 'batch_size': 128, 'epochs': 20, 'learning_rate': 0.0001}.\n",
            "\n"
          ]
        }
      ],
      "source": [
        "\"\"\"\n",
        "    The custom Attention Layer:\n",
        "\"\"\"\n",
        "# Add attention layer to the deep learning network\n",
        "class attention(Layer):\n",
        "    def __init__(self,**kwargs):\n",
        "        super(attention,self).__init__(**kwargs)\n",
        "\n",
        "    def build(self,input_shape):\n",
        "        self.W=self.add_weight(name='attention_weight', shape=(input_shape[-1],1),\n",
        "                               initializer='random_normal', trainable=True)\n",
        "        self.b=self.add_weight(name='attention_bias', shape=(input_shape[1],1),\n",
        "                               initializer='zeros', trainable=True)\n",
        "        super(attention, self).build(input_shape)\n",
        "\n",
        "    def call(self,x):\n",
        "        # Alignment scores. Pass them through tanh function\n",
        "        e = K.tanh(K.dot(x,self.W)+self.b)\n",
        "        # Remove dimension of size 1\n",
        "        e = K.squeeze(e, axis=-1)\n",
        "        # Compute the weights\n",
        "        alpha = K.softmax(e)\n",
        "        # Reshape to tensorFlow format\n",
        "        alpha = K.expand_dims(alpha, axis=-1)\n",
        "        # Compute the context vector\n",
        "        context = x * alpha\n",
        "        context = K.sum(context, axis=1)\n",
        "        return context\n",
        "\n",
        "\"\"\"\n",
        "   Model 5 - Hybrid CNN_BiLSTM Network With Attention Layer:\n",
        "\"\"\"\n",
        "# Compile the Model:\n",
        "def call_existing_code(hp, optimizer, batch_size, epochs):\n",
        "\n",
        "    model = Sequential()\n",
        "    model.add(Conv1D(filters=128, kernel_size=6, activation='relu',\n",
        "                    padding='same', input_shape=(187, 1)))\n",
        "    model.add(BatchNormalization())\n",
        "\n",
        "    # adding a pooling layer\n",
        "    model.add(MaxPooling1D(pool_size=(3), strides=2, padding='same'))\n",
        "\n",
        "    model.add(Conv1D(filters=64, kernel_size=6, activation='relu',\n",
        "                    padding='same', input_shape=(187, 1)))\n",
        "    model.add(BatchNormalization())\n",
        "    model.add(MaxPooling1D(pool_size=(3), strides=2, padding='same'))\n",
        "\n",
        "    model.add(Conv1D(filters=64, kernel_size=6, activation='relu',\n",
        "                    padding='same', input_shape=(187, 1)))\n",
        "    model.add(BatchNormalization())\n",
        "    model.add(MaxPooling1D(pool_size=(3), strides=2, padding='same'))\n",
        "\n",
        "    model.add(Bidirectional(LSTM(256, return_sequences=True)))\n",
        "    model.add(attention())\n",
        "    model.add(Dropout(0.2))\n",
        "    model.add(Dense(64, activation='relu'))\n",
        "    model.add(Dropout(0.2))\n",
        "    model.add(Dense(5, activation='softmax'))\n",
        "\n",
        "    hp_learning_rate = hp.Choice('learning_rate', values=[1e-2, 1e-3, 1e-4])\n",
        "    model.compile(loss='categorical_crossentropy',\n",
        "                  optimizer = keras.optimizers.Adam(learning_rate=hp_learning_rate),\n",
        "                  metrics=['accuracy', f1_m, precision_m, recall_m])\n",
        "    return model\n",
        "\n",
        "def build_model(hp):\n",
        "    optimizer = hp.Choice(\"optimizer\", [\"adam\", \"sgd\"]),\n",
        "    batch_size = hp.Int('batch_size', 32, 256, step=32),\n",
        "    epochs = hp.Int('epochs', 10, 100, step=10),\n",
        "\n",
        "    # call existing model-building code with the hyperparameter values.\n",
        "    model = call_existing_code(hp, optimizer=optimizer, batch_size=batch_size,\n",
        "                               epochs=epochs)\n",
        "    return model\n",
        "\n",
        "build_model(keras_tuner.HyperParameters())\n",
        "\n",
        "callbacks = [\n",
        "    keras.callbacks.ModelCheckpoint(\n",
        "        \"best_model_CNN_BiLSTM_Attention.h5\", save_best_only=True, monitor=\"val_loss\"\n",
        "    ),\n",
        "    keras.callbacks.EarlyStopping(monitor=\"val_loss\", patience=50, verbose=1),\n",
        "    ]\n",
        "\n",
        "tuner = keras_tuner.RandomSearch(\n",
        "    hypermodel = build_model,\n",
        "    objective=\"val_accuracy\",\n",
        "    max_trials=2,\n",
        "    executions_per_trial=1,\n",
        "    overwrite=True,\n",
        "    directory=\"my_dir\",\n",
        "    project_name=\"CNN_BiLSTM_Attention_MITBIH\",\n",
        ")\n",
        "\n",
        "# print a summary of the search space:\n",
        "tuner.search_space_summary()\n",
        "\n",
        "tuner.search(\n",
        "    X_train, y_train, validation_split=0.3, epochs = 10,\n",
        ")\n",
        "\n",
        "print(tuner.results_summary())\n",
        "\n",
        "# Get the optimal hyperparameters\n",
        "best_hp = tuner.get_best_hyperparameters()[0]\n",
        "\n",
        "# Get the best model:\n",
        "new_model = tuner.get_best_models()[0]\n",
        "\n",
        "IPython.display.clear_output()\n",
        "\n",
        "print(f\"\"\"\n",
        "The hyperparameter search is complete. The optimal learning rate for the optimizer\n",
        "is {best_hp.get('learning_rate')}, the optimal Optimizer is {best_hp.get('optimizer')},\n",
        "the optimal batch size for the optimizer is {best_hp.get('batch_size')}, the optimal epoch for the optimizer\n",
        "is {best_hp.get('epochs')}, and the Best Hyperparameters values are {best_hp.values}.\n",
        "\"\"\")"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\"\"\"\n",
        "    Retrain the model:\n",
        "\"\"\"\n",
        "# Build the model with the best hp.\n",
        "\n",
        "model = new_model\n",
        "\n",
        "# Fit with the entire dataset.\n",
        "model.fit(X_train, y_train, validation_split=0.3, epochs = 5,\n",
        "          callbacks=callbacks,\n",
        "          verbose=1,\n",
        "          )\n",
        "\n",
        "# Evaluate the model\n",
        "performance['CNN_BiLSTM_Attention_MITBIH'] = model.evaluate(X_test, y_test, verbose = 0)\n",
        "print(\"[loss:, accuracy :, f1_m :,  precision_m :, recall_m :]\", performance['CNN_BiLSTM_Attention_MITBIH'])\n",
        "\n",
        "# Evaluate the model\n",
        "loss, accuracy, f1_score, precision, recall = model.evaluate(X_test, y_test, verbose=0)\n",
        "print(\"Test accuracy\", accuracy)\n",
        "print(\"Test F1-Score\", f1_score)\n",
        "print(\"Test Precision\", precision)\n",
        "print(\"Test Recall\", recall)\n",
        "\n",
        "# making predictions on test set\n",
        "start = time.time()\n",
        "y_pred_CNN_BiLSTM_Attention = model.predict(X_test)\n",
        "end = time.time()\n",
        "print('Time Taken: %.3f seconds' % (end-start))\n",
        "\n",
        "model5 = model\n",
        "all_models.append(model5)\n",
        "\n",
        "model5.summary()\n",
        "\n",
        "keras.utils.plot_model(model5, show_shapes=True)\n",
        "\n",
        "# Plot Model's Confusion Matrix:\n",
        "\n",
        "def plot_confusion_matrix(cm, classes,\n",
        "                          normalize=False,\n",
        "                          title='Confusion matrix',\n",
        "                          cmap=plt.cm.Blues):\n",
        "    if normalize:\n",
        "        cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\n",
        "        print(\"Normalized confusion matrix\")\n",
        "    else:\n",
        "        print('Confusion matrix, without normalization')\n",
        "\n",
        "    plt.imshow(cm, interpolation='nearest', cmap=cmap)\n",
        "    plt.title(title, size = 20, fontweight='bold')\n",
        "    plt.colorbar()\n",
        "    tick_marks = np.arange(len(classes))\n",
        "    plt.xticks(tick_marks, classes, rotation=45, fontsize=18)\n",
        "    plt.yticks(tick_marks, classes, fontsize=18)\n",
        "\n",
        "    fmt = '.2f' if normalize else 'd'\n",
        "    thresh = cm.max() / 2.\n",
        "    for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\n",
        "        plt.text(j, i, format(cm[i, j], fmt),\n",
        "                 horizontalalignment=\"center\",\n",
        "                 color=\"white\" if cm[i, j] > thresh else \"black\")\n",
        "\n",
        "    plt.tight_layout()\n",
        "    plt.ylabel('True Label', weight='bold').set_fontsize('18')\n",
        "    plt.xlabel('Predicted Label', weight='bold').set_fontsize('18')\n",
        "    plt.savefig('Confusion Matrix for CNN_BiLSTM_Attention Model.png')\n",
        "\n",
        "cf_matrix = confusion_matrix(y_test.argmax(axis=1), y_pred_CNN_BiLSTM_Attention.argmax(axis=1))\n",
        "plt.figure(figsize=(15, 12))\n",
        "plot_confusion_matrix(cf_matrix, classes=['N', 'S', 'V', 'F', 'Q'],\n",
        "                      normalize = True, title='Confusion Matrix With Normalization - CNN_BiLSTM_Attention Model')\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "v8d4EwU8oJLj",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "798380d2-2963-4913-a6c6-8e8341e8ae94"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/5\n",
            "2188/2188 [==============================] - 470s 213ms/step - loss: 0.0453 - accuracy: 0.9855 - f1_m: 0.9853 - precision_m: 0.9858 - recall_m: 0.9849 - val_loss: 15.8812 - val_accuracy: 0.3333 - val_f1_m: 0.3332 - val_precision_m: 0.3332 - val_recall_m: 0.3332\n",
            "Epoch 2/5\n",
            "2188/2188 [==============================] - 465s 213ms/step - loss: 0.0389 - accuracy: 0.9877 - f1_m: 0.9876 - precision_m: 0.9880 - recall_m: 0.9872 - val_loss: 19.3066 - val_accuracy: 0.3268 - val_f1_m: 0.3266 - val_precision_m: 0.3266 - val_recall_m: 0.3266\n",
            "Epoch 3/5\n",
            "2188/2188 [==============================] - 466s 213ms/step - loss: 0.0345 - accuracy: 0.9889 - f1_m: 0.9890 - precision_m: 0.9892 - recall_m: 0.9887 - val_loss: 20.4239 - val_accuracy: 0.3311 - val_f1_m: 0.3310 - val_precision_m: 0.3310 - val_recall_m: 0.3310\n",
            "Epoch 4/5\n",
            "2188/2188 [==============================] - 469s 214ms/step - loss: 0.0331 - accuracy: 0.9888 - f1_m: 0.9888 - precision_m: 0.9891 - recall_m: 0.9886 - val_loss: 21.1361 - val_accuracy: 0.3265 - val_f1_m: 0.3263 - val_precision_m: 0.3268 - val_recall_m: 0.3257\n",
            "Epoch 5/5\n",
            "2188/2188 [==============================] - 469s 214ms/step - loss: 0.0288 - accuracy: 0.9907 - f1_m: 0.9907 - precision_m: 0.9909 - recall_m: 0.9904 - val_loss: 21.5913 - val_accuracy: 0.3328 - val_f1_m: 0.3326 - val_precision_m: 0.3326 - val_recall_m: 0.3326\n",
            "[loss:, accuracy :, f1_m :,  precision_m :, recall_m :] [2.482186794281006, 0.8921067118644714, 0.890923023223877, 0.8916323781013489, 0.8902372121810913]\n",
            "Test accuracy 0.8921067118644714\n",
            "Test F1-Score 0.890923023223877\n",
            "Test Precision 0.8916323781013489\n",
            "Test Recall 0.8902372121810913\n",
            "Time Taken: 30.991 seconds\n",
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " conv1d (Conv1D)             (None, 187, 128)          896       \n",
            "                                                                 \n",
            " batch_normalization (BatchN  (None, 187, 128)         512       \n",
            " ormalization)                                                   \n",
            "                                                                 \n",
            " max_pooling1d (MaxPooling1D  (None, 94, 128)          0         \n",
            " )                                                               \n",
            "                                                                 \n",
            " conv1d_1 (Conv1D)           (None, 94, 64)            49216     \n",
            "                                                                 \n",
            " batch_normalization_1 (Batc  (None, 94, 64)           256       \n",
            " hNormalization)                                                 \n",
            "                                                                 \n",
            " max_pooling1d_1 (MaxPooling  (None, 47, 64)           0         \n",
            " 1D)                                                             \n",
            "                                                                 \n",
            " conv1d_2 (Conv1D)           (None, 47, 64)            24640     \n",
            "                                                                 \n",
            " batch_normalization_2 (Batc  (None, 47, 64)           256       \n",
            " hNormalization)                                                 \n",
            "                                                                 \n",
            " max_pooling1d_2 (MaxPooling  (None, 24, 64)           0         \n",
            " 1D)                                                             \n",
            "                                                                 \n",
            " bidirectional (Bidirectiona  (None, 24, 512)          657408    \n",
            " l)                                                              \n",
            "                                                                 \n",
            " attention (attention)       (None, 512)               536       \n",
            "                                                                 \n",
            " dropout (Dropout)           (None, 512)               0         \n",
            "                                                                 \n",
            " dense (Dense)               (None, 64)                32832     \n",
            "                                                                 \n",
            " dropout_1 (Dropout)         (None, 64)                0         \n",
            "                                                                 \n",
            " dense_1 (Dense)             (None, 5)                 325       \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 766,877\n",
            "Trainable params: 766,365\n",
            "Non-trainable params: 512\n",
            "_________________________________________________________________\n",
            "Normalized confusion matrix\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1080x864 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "F7UhDaYez7kh",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 666
        },
        "outputId": "ecf345fe-3c7f-4b04-9606-9b4a9876fad1"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1296x432 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "\"\"\"\n",
        "    Performance ACCURACY:\n",
        "\"\"\"\n",
        "x = np.arange(len(performance))\n",
        "width = 0.5\n",
        "metric_name = 'accuracy'\n",
        "metric_index = model.metrics_names.index('accuracy')\n",
        "test_accuracy = [v[metric_index] for v in performance.values()]\n",
        "\n",
        "plt.figure(figsize=(18, 6))\n",
        "plt.ylabel(\"Accuracy\", weight='bold').set_fontsize('18')\n",
        "#plt.bar(x - 0.17, val_accuracy, width, label='Validation')\n",
        "plt.bar(x + 0.17, test_accuracy, width, label='Test', color = 'darkorange')\n",
        "plt.xticks(ticks=x, labels=performance.keys(),\n",
        "           rotation=90, size = 16, fontweight='bold')\n",
        "plt.title('Performance Comparison of All DL Models - Accuracy', size = 22, fontweight='bold')\n",
        "_ = plt.legend(loc = 2, bbox_to_anchor = (1.0, 1.0), ncol = 1, frameon=True, fontsize = '22')\n",
        "plt.grid(True)\n",
        "plt.savefig('Performance Comparison of All DL Models-Accuracy.png')"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\"\"\"\n",
        "    Performance F1-SCORE:\n",
        "\"\"\"\n",
        "x = np.arange(len(performance))\n",
        "width = 0.5\n",
        "metric_name = f1_m\n",
        "metric_index = model.metrics_names.index('f1_m')\n",
        "test_accuracy = [v[metric_index] for v in performance.values()]\n",
        "\n",
        "plt.figure(figsize=(18, 6))\n",
        "plt.ylabel(\"F1-Score\", weight='bold').set_fontsize('18')\n",
        "#plt.bar(x - 0.17, val_accuracy, width, label='Validation')\n",
        "plt.bar(x + 0.17, test_accuracy, width, label='Test', color = 'darkorange')\n",
        "plt.xticks(ticks=x, labels=performance.keys(),\n",
        "           rotation=90, size = 16, fontweight='bold')\n",
        "plt.title('Performance Comparison of All DL Models - F1-Score', size = 22, fontweight='bold')\n",
        "_ = plt.legend(loc = 2, bbox_to_anchor = (1.0, 1.0), ncol = 1, frameon=True, fontsize = '22')\n",
        "plt.grid(True)\n",
        "plt.savefig('Performance Comparison of All DL Models-F1-Score.png')"
      ],
      "metadata": {
        "id": "jiBj9YeLrN9C",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "098b2947-1263-4609-b69c-194c1b776557"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "' \\n    Performance F1-SCORE:\\n'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\"\"\"\n",
        "    Performance PRECISION:\n",
        "\"\"\"\n",
        "x = np.arange(len(performance))\n",
        "width = 0.5\n",
        "metric_name = precision_m\n",
        "metric_index = model.metrics_names.index('precision_m')\n",
        "test_accuracy = [v[metric_index] for v in performance.values()]\n",
        "\n",
        "plt.figure(figsize=(18, 6))\n",
        "plt.ylabel(\"Precision\", weight='bold').set_fontsize('18')\n",
        "#plt.bar(x - 0.17, val_accuracy, width, label='Validation')\n",
        "plt.bar(x + 0.17, test_accuracy, width, label='Test', color = 'darkorange')\n",
        "plt.xticks(ticks=x, labels=performance.keys(),\n",
        "           rotation=90, size = 16, fontweight='bold')\n",
        "plt.title('Performance Comparison of All DL Models - Precision', size = 22, fontweight='bold')\n",
        "_ = plt.legend(loc = 2, bbox_to_anchor = (1.0, 1.0), ncol = 1, frameon=True, fontsize = '22')\n",
        "plt.grid(True)\n",
        "plt.savefig('Performance Comparison of All DL Models-Precision.png')"
      ],
      "metadata": {
        "id": "d2pZ_YEzsf2Z"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "\"\"\"\n",
        "    Performance RECALL:\n",
        "\"\"\"\n",
        "x = np.arange(len(performance))\n",
        "width = 0.5\n",
        "metric_name = recall_m\n",
        "metric_index = model.metrics_names.index('recall_m')\n",
        "test_accuracy = [v[metric_index] for v in performance.values()]\n",
        "\n",
        "plt.figure(figsize=(18, 6))\n",
        "plt.ylabel(\"Recall\", weight='bold').set_fontsize('18')\n",
        "#plt.bar(x - 0.17, val_accuracy, width, label='Validation')\n",
        "plt.bar(x + 0.17, test_accuracy, width, label='Test', color = 'darkorange')\n",
        "plt.xticks(ticks=x, labels=performance.keys(),\n",
        "           rotation=90, size = 16, fontweight='bold')\n",
        "plt.title('Performance Comparison of All DL Models - Recall', size = 22, fontweight='bold')\n",
        "_ = plt.legend(loc = 2, bbox_to_anchor = (1.0, 1.0), ncol = 1, frameon=True, fontsize = '22')\n",
        "plt.grid(True)\n",
        "plt.savefig('Performance Comparison of All DL Models-Recall.png')"
      ],
      "metadata": {
        "id": "DsZ93BcRs2Mf"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "\"\"\"\n",
        "    Performance Comparison between All ML and DL Models in terms of all Metrics:\n",
        "\"\"\"\n",
        "SVC_Model = [0.8423, 0.8986, 0.8525, 0.9625]\n",
        "CNN_Model = [0.9115, 0.9104, 0.9102, 0.9106]\n",
        "CNN_BiLSTM_Model = [0.8080, 0.7966, 0.7748, 0.8204]\n",
        "Attention_CNN_BiLSTM_Model = [0.8921, 0.8909, 0.8902, 0.8916]\n",
        "\n",
        "N = 4\n",
        "x = np.arange(N)\n",
        "c = ['red', 'orange', 'blue', 'green']\n",
        "width = 0.5\n",
        "\n",
        "# Set position of bar on X axis\n",
        "r1 = np.arange(0,len(CNN_Model) * 3, 3)\n",
        "r2 = [x + width for x in r1]\n",
        "r3 = [x + width for x in r2]\n",
        "r4 = [x + width for x in r3]\n",
        "\n",
        "plt.figure(figsize=(15, 8))\n",
        "plt.ylabel(\"Error\", weight='bold').set_fontsize('18')\n",
        "plt.bar(r1, SVC_Model, width, edgecolor='white', label='SVC_Model')\n",
        "plt.bar(r2, CNN_Model, width, edgecolor='white', label='CNN_Model')\n",
        "plt.bar(r3, CNN_BiLSTM_Model, width, edgecolor='white', label='CNN_BiLSTM_Model')\n",
        "plt.bar(r4, Attention_CNN_BiLSTM_Model, width, edgecolor='white', label='Attention_CNN_BiLSTM_Model')\n",
        "plt.xlabel('METRICS', weight='bold').set_fontsize('18')\n",
        "plt.xticks([r + width for r in range(0, len(SVC_Model) * 3, 3)], ['ACCURACY', 'F1-SCORE', 'RECALL', 'PRECISION'], rotation=45, size = 12, fontweight='bold')\n",
        "plt.title('Performance Comparison between All ML and DL Models in terms of all Metrics', size = 22, fontweight='bold')\n",
        "plt.legend(loc = 'best', bbox_to_anchor = (1.0, 1.0), ncol = 1, frameon=True, fontsize = '12')\n",
        "plt.savefig('Performance Comparison between All ML and DL Models in terms of all Metrics.png')"
      ],
      "metadata": {
        "id": "QQwBeLI9quc2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 578
        },
        "outputId": "40ef0705-3e2e-44b5-fab7-aa2144d9ec4b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1080x576 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\"\"\"\n",
        "    Performance Comparison between All ML and DL Models in terms of all Metrics:\n",
        "\"\"\"\n",
        "SVC_Model = [0.8423, 0.8986, 0.8525, 0.9625]\n",
        "\n",
        "N = 4\n",
        "x = np.arange(N)\n",
        "c = ['red']\n",
        "width = 0.9\n",
        "\n",
        "# Set position of bar on X axis\n",
        "r1 = np.arange(0, len(CNN_Model) * 3, 3)\n",
        "\n",
        "plt.figure(figsize=(15, 8))\n",
        "plt.ylabel(\"Error\", weight='bold').set_fontsize('18')\n",
        "plt.bar(r1, SVC_Model, width, edgecolor='white', label='SVC_Model', color = 'red')\n",
        "plt.xlabel('METRICS', weight='bold').set_fontsize('18')\n",
        "plt.xticks([r + width/4 for r in range(0, len(SVC_Model) * 3, 3)], ['ACCURACY', 'F1-SCORE', 'RECALL', 'PRECISION'], rotation=20, size = 12, fontweight='bold')\n",
        "plt.title('Performance Comparison between the ML Model in terms of all Metrics', size = 22, fontweight='bold')\n",
        "plt.legend(loc = 'best', bbox_to_anchor = (1.0, 1.0), ncol = 1, frameon=True, fontsize = '12')\n",
        "plt.savefig('Performance Comparison between the ML Model in terms of all Metrics.png')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 554
        },
        "id": "xx6yBmOyPzqS",
        "outputId": "9ac6d223-b98a-4090-9adb-42614e58da6b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1080x576 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\"\"\"\n",
        "    Performance Comparison between All DL Models in terms of all Metrics:\n",
        "\"\"\"\n",
        "CNN_Model = [0.9115, 0.9104, 0.9102, 0.9106]\n",
        "CNN_BiLSTM_Model = [0.8080, 0.7966, 0.7748, 0.8204]\n",
        "Attention_CNN_BiLSTM_Model = [0.8921, 0.8909, 0.8902, 0.8916]\n",
        "\n",
        "N = 4\n",
        "x = np.arange(N)\n",
        "c = ['red', 'blue', 'green']\n",
        "width = 0.5\n",
        "\n",
        "# Set position of bar on X axis\n",
        "r1 = np.arange(0,len(CNN_Model) * 3, 3)\n",
        "r2 = [x + width for x in r1]\n",
        "r3 = [x + width for x in r2]\n",
        "\n",
        "plt.figure(figsize=(15, 8))\n",
        "plt.ylabel(\"Error\", weight='bold').set_fontsize('18')\n",
        "plt.bar(r1, CNN_Model, width, edgecolor='white', label='CNN_Model')\n",
        "plt.bar(r2, CNN_BiLSTM_Model, width, edgecolor='white', label='CNN_BiLSTM_Model')\n",
        "plt.bar(r3, Attention_CNN_BiLSTM_Model, width, edgecolor='white', label='Attention_CNN_BiLSTM_Model')\n",
        "plt.xlabel('METRICS', weight='bold').set_fontsize('18')\n",
        "plt.xticks([r + width for r in range(0, len(SVC_Model) * 3, 3)], ['ACCURACY', 'F1-SCORE', 'RECALL', 'PRECISION'], rotation=45, size = 12, fontweight='bold')\n",
        "plt.title('Performance Comparison between All DL Models in terms of all Metrics', size = 22, fontweight='bold')\n",
        "plt.legend(loc = 'best', bbox_to_anchor = (1.0, 1.0), ncol = 1, frameon=True, fontsize = '12')\n",
        "plt.savefig('Performance Comparison between All DL Models in terms of all Metrics.png')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 578
        },
        "id": "ToHZTEDpTI5n",
        "outputId": "6c576ad7-5172-4f7e-8752-32475a19cbbc"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1080x576 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    }
  ],
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}